Silicon is the second most abundant element in soils and is beneficial for plant growth. Although, the localizations and polarities of rice Si transporters have been elucidated, the mechanisms that control the expression of Si transporter genes and the functional reasons for controlling expression are not well-understood. We developed a new model that simulates the dynamics of Si in the whole plant in rice by considering Si transport in the roots, distribution at the nodes, and signaling substances controlling transporter gene expression. To investigate the functional reason for the diurnal variation of the expression level, we compared investment efficiencies (the amount of Si accumulated in the upper leaf divided by the total expression level of Si transporter genes) at different model settings. The model reproduced the gradual decrease and diurnal variation of the expression level of the transporter genes observed by previous experimental studies. The results of simulation experiments showed that a considerable reduction in the expression of Si transporter genes during the night increases investment efficiency. Our study suggests that rice has a system that maximizes the investment efficiency of Si uptake.

Little is known about the network structure of competition in large populations of plants, despite the importance of such knowledge for understanding population dynamics. In this study, we used complex network analysis to examine temporal changes in the network structure of competition in an even-aged multi-individual stand of the Sakhalin fir Abies sachalinensis in Hokkaido, Japan. Using census data, which were measured over 30 years (1948-1978; seedlings were planted in 1929), on the sizes and locations of these plants, we regarded a plant as a node and competition between plants as a link. We then introduced two indices, the binary and weighted out-degrees (BO and WO, respectively), to interpret complicated plant interactions. The BO of a plant represents the number of links from the target plant to its neighbors, and the WO is the total strength of competition from the target plant to its neighbors. The analysis showed that the distributions of BO and WO were heavy-tailed in all years and that large plants had large BO and WO. These results suggest that only a few (i.e., large) plants have a very large impact on the growth and survival of a much larger number of neighboring plants and thus on population dynamics, whereas most of the others (i.e., small and medium-sized plants) have only a small impact on a few neighbors. By introducing binary and weighted connectivities (BC and WC, respectively), we were able to identify the size classes of neighbors with which the target plant preferentially and strongly competed. The BC and WC results showed that large plants competed preferentially and more strongly with other large plants in 1948, but they competed more strongly with small plants after 1963. These results clarify targets of the very large impact of large plants, as shown by the results of BO and WO: the impact was exerted on the growth and survival of other large plants in 1948, whereas the impact was exerted on those of small plants after 1963. Our study demonstrates that the statistical properties of the competition network structure, which have been largely ignored in plant competition research, are important for understanding plant population dynamics. (C) 2016 Elsevier B.V. All rights reserved.

We examined the indirect positive effect of one plant on the growth of another via growth suppression of a shared competing plant as an indirect facilitation between plants. We simulated the dynamics of populations of 3600 competing plants and used absolute interaction intensity (AIIi,j), the causal effect of plant j on plant i. The positive effect due to indirect facilitation between two plants occurs at a short distance apart and increases with increasing average density. The indirect facilitation between plants becomes increasingly important with increasing average density to explain the dynamics of size structure in plant populations.

Rice productivity will be affected by climate conditions not only in own region but also in neighboring regions through technological spillover. Measuring such direct and indirect influence of future climate change is important for policy making. This study analyzes socioeconomic and climate factors in rice total factor productivity (TFP) and evaluates technological spillover effects by using the spatial econometric model. To consider geographical situation, we use hydrological model in addition to crop-yield and crop-quality models. Results show that spatial autoregressive tendencies were observed in rice TFP, even though the influences of climate factors were removed. Such spatial dependence brings about synergistic effects among neighboring prefectures in northern Japan and depression effects, like a spatial trap, from neighbors in southern Japan. Substantial impacts of climate change were as high as socio-economic factors but different in degrees by regions. Also, future climate change estimated by the global climate model enlarged fluctuation degree in rice TFP because accumulative or cancel out effects of temperature and precipitation occurred year by year. Therefore, technological development in rice production and provision of precise climate prediction to farmers are important in order to ease and mitigate these influences.

Most terrestrial plant communities exhibit relatively high species diversity and many competitive species are ubiquitous. Many theoretical studies have been carried out to investigate the coexistence of a few competitive species and in most cases they suggest competitive exclusion. Theoretical studies have revealed that coexistence of even three or four species can be extremely difficult. It has been suggested that the coexistence of many species has been achieved by the fine differences in suitable microhabitats for each species, attributing to niche-separation. So far there is no explicit demonstration of such a coexistence in mathematical and simulation studies. Here we built a simple lattice Lotka-Volterra model of competition by incorporating the minute differences of suitable microhabitats for many species. By applying the site variations in species-specific settlement rates of a seedling, we achieved the coexistence of more than 10 species. This result indicates that competition between many species is avoided by the spatial variations in species-specific microhabitats. Our results demonstrate that coexistence of many species becomes possible by the minute differences in microhabitats. This mechanism should be applicable to many vegetation types, such as temperate forests and grasslands.

Replacing a rainfed cropping system with an irrigated one is widely assumed to be an effective measure for climate change adaptation. However, many agricultural impact studies have not necessarily accounted for the space-time variations in the water availability under changing climate and land use. Moreover, many hydrologic and agricultural assessments of climate change impacts are not fully integrated. To overcome this shortcoming, a tool that can simultaneously simulate the dynamic interactions between crop production and water resources in a watershed is essential. Here we propose the regional production and circulation coupled model (CROVER) by embedding the PRYSBI-2 (Process-based Regional Yield Simulator with Bayesian Inference version 2) large-area crop model into the global water resources model (called H08), and apply this model to the Songhua River watershed in Northeast China. The evaluation reveals that the model's performance in capturing the major characteristics of historical change in surface soil moisture, river discharge, actual crop evapotranspiration, and soybean yield relative to the reference data during the interval 1979-2010 is satisfactory accurate. The simulation experiments using the model demonstrated that subregional irrigation management, such as designating the area to which irrigation is primarily applied, has measurable influences on the regional crop production in a drought year. This finding suggests that reassessing climate change risk in agriculture using this type of modeling is crucial not to overestimate potential of irrigation-based adaptation.

Replacing a rainfed cropping system with an irrigated one is widely assumed to be an effective measure for climate change adaptation. However, many agricultural impact studies have not necessarily accounted for the space-time variations in the water availability under changing climate and land use. Moreover, many hydrologic and agricultural assessments of climate change impacts are not fully integrated. To overcome this shortcoming, a tool that can simultaneously simulate the dynamic interactions between crop production and water resources in a watershed is essential. Here we propose the regional production and circulation coupled model (CROVER) by embedding the PRYSBI-2 (Process-based Regional Yield Simulator with Bayesian Inference version 2) large-area crop model into the global water resources model (called H08), and apply this model to the Songhua River watershed in Northeast China. The evaluation reveals that the model's performance in capturing the major characteristics of historical change in surface soil moisture, river discharge, actual crop evapotranspiration, and soybean yield relative to the reference data during the interval 1979-2010 is satisfactory accurate. The simulation experiments using the model demonstrated that subregional irrigation management, such as designating the area to which irrigation is primarily applied, has measurable influences on the regional crop production in a drought year. This finding suggests that reassessing climate change risk in agriculture using this type of modeling is crucial not to overestimate potential of irrigation-based adaptation.

Silicon (Si) uptake by the roots is mediated by two different transporters, Lsi1 (passive) and Lsi2 (active), in rice (Oryza sativa). Both transporters are polarly localized in the plasma membranes of exodermal (outer) and endodermal (inner) cells with Casparian strips. However, it is unknown how rice is able to take up large amounts of Si compared with other plants, and why rice Si transporters have a characteristic cellular localization pattern. To answer these questions, we simulated Si uptake by rice roots by developing a mathematical model based on a simple diffusion equation that also accounts for active transport by Lsi2. In this model, we calibrated the model parameters using in vivo experimental data on the Si concentrations in the xylem sap and a Monte Carlo method. In our simulation experiments, we compared the Si uptake between roots with various transporter and Casparian strip locations and estimated the Si transport efficiency of roots with different localization patterns and quantities of the Lsi transporters. We found that the Si uptake by roots that lacked Casparian strips was lower than that of normal roots. This suggests that the doublelayer structure of the Casparian strips is an important factor in the high Si uptake by rice. We also found that among various possible localization patterns, the most efficient one was that of the wild-type rice; this may explain the high Si uptake capacity of rice.

We re-examined traditional explanations regarding relationships between competition among plants and spatial patterns. We focused particularly on the prevailing view, which is that competition between smaller plants and larger plants serves only as a repulsive force between neighbors, and always decreases the degree of aggregation between smaller plants and larger plants over time. We propose an alternative underlying mechanism explaining the observed spatial patterns using a spatially explicit, individualbased model with general assumptions regarding the nature of competition among plants. We statistically estimated parameters for the model from observed census data collected over 30 years in an even-aged experimental fir forest (Abies sachalinensis). The results of our simulations, based on field data, indicated that asymmetric competition among plants led to the aggregation of smaller plants around a larger plant (i.e., not toward a uniform spatial pattern). This spatial pattern was generated by the growth suppression of plants near larger plants during the early growth stages, and more importantly, by the existence of a zone with lower competition intensity (referred to as competition-induced shelter, CiS) around a larger plant after the early growth stages. Larger plants compete for resources with large and medium-sized neighbors to the extent that the neighbors die and are removed, resulting in CiS. The results also indicate that competition between smaller plants and larger plants in an even-aged population exerts not only the traditionally recognized repulsive force but also a pseudo-attractive force, such as CiS, which promotes aggregation of smaller plants around a larger plant. (C) 2015 Elsevier B.V. All rights reserved.

Carbon dioxide (CO2) efflux from the soil surface, which is a major source of CO2 from terrestrial ecosystems, represents the total CO2 production at all soil depths. Although many studies have estimated the vertical profile of the CO2 production rate, one of the difficulties in estimating the vertical profile is measuring diffusion coefficients of CO2 at all soil depths in a nondestructive manner. In this study, we estimated the temporal variation in the vertical profile of the CO2 production rate using a data assimilation method, the particle filtering method, in which the diffusion coefficients of CO2 were simultaneously estimated. The CO2 concentrations at several soil depths and CO2 efflux from the soil surface (only during the snow-free period) were measured at two points in a broadleaf forest in Japan, and the data were assimilated into a simple model including a diffusion equation. We found that there were large variations in the pattern of the vertical profile of the CO2 production rate between experiment sites: the peak CO2 production rate was at soil depths around 10 cm during the snow-free period at one site, but the peak was at the soil surface at the other site. Using this method to estimate the CO2 production rate during snow-cover periods allowed us to estimate CO2 efflux during that period as well. We estimated that the CO2 efflux during the snow-cover period (about half the year) accounted for around 13% of the annual CO2 efflux at this site. Although the method proposed in this study does not ensure the validity of the estimated diffusion coefficients and CO2 production rates, the method enables us to more closely approach the "actual" values by decreasing the variance of the posterior distribution of the values.

To understand how terrestrial ecosystems respond to global climate change, researchers have globally measured the energy, water and carbon dioxide flux densities (F) globally over various types of vegetation by the eddy covariance (EC) method. However, the process of F calculation and the method of quality control and quality assurance (QCQA) are complex and site specific. Moreover, instantly maintaining remote EC flux measurement sites against instrumentation problems and administrative difficulties is laborious. To overcome these issues, particularly those of realtime F monitoring and prompt site management, FluxPro was created.
FluxPro consists of three functional systems: 1) a gathering system that transports EC measurements from various sites to the FluxPro management server; 2) a cooking system that computes F and its frictional uncertainty (a) together with micrometeorological variables (V); and 3) a serving system that presents the results of the gathering and cooking systems as charts to be distributed over the internet in realtime. Consequently, FluxPro could become an appropriate system for realtime-multi-site management, since it not only automatically monitors F with a and V but also continuously surveils EC sites, including copious information and an email alert system.

Under global warming, influences of heat stress and flooding on rice production are becoming critical in Japan, but it is still under discussion whether future climate change is beneficial or harmful for Japanese rice production. This study aims to evaluate the impacts of long-term climate change on rice total factor productivity (TFP) by a panel data analysis. We estimate a regression model to link rice TFP to climate factors via yield, quality, and flood influence by using crop models, and then project future TFP levels from the results of the high-resolution model for interdisciplinary research on climate (MIROC). The results demonstrate that climate change has a positive effect in the northern regions, such as Hokkaido and Tohoku, but it decreases rice TFP in other regions, especially in the western regions, after the 2050s. Furthermore, climate change increases fluctuations in rice TFP of the western regions. To overcome negative impacts, a forward shift in the rice transplanting time is effective. Second, the potential impacts of climate factors, shown by the elasticity values of rice TFP, are 0.18 (via yield), 0.09 (via quality), and -0.03 (via flood influence), but these climate impacts are weaker than socio-economic factors, such as economies of scale and research and development capital stocks. Third, regional gaps in rice TFP are enlarged over time because of different impacts of climate factors as well as socio-economic factors. Such fact-findings can be used to reconsider agricultural policy.

Reliable regional-scale representation of crop growth and yields has been increasingly important in earth system modeling for the simulation of atmosphere-vegetation-soil interactions in managed ecosystems. While the parameter values in many crop models are location specific or cultivar specific, the validity of such values for regional simulation is in question. We present the scale dependency of likely parameter values that are related to the responses of growth rate and yield to temperature, using the paddy rice model applied to Japan as an example. For all regions, values of the two parameters that determine the degree of yield response to low temperature (the base temperature for calculating cooling degree days and the curvature factor of spikelet sterility caused by low temperature) appeared to change relative to the grid interval. Two additional parameters (the air temperature at which the developmental rate is half of the maximum rate at the optimum temperature and the value of developmental index at which point the crop becomes sensitive to the photoperiod) showed scale dependency in a limited region, whereas the remaining three parameters that determine the phenological characteristics of a rice cultivar and the technological level show no clear scale dependency. These results indicate the importance of using appropriate parameter values for the spatial scale at which a crop model operates. We recommend avoiding the use of location-specific or cultivar-specific parameter values for regional crop simulation, unless a rationale is presented suggesting these values are insensitive to spatial scale.

We developed a crop scheduling model for rice cultivation in the Vietnam Mekong Delta (VMD), focusing on the adaptive behavior of crop planning to various water resource constraints. In addition, we also examined the effects of environmental change on rice cultivation in the last decade. In the VMD, multiple rice cropping is practiced under a variety of adverse water conditions, including flooding, salinity intrusion, and irregular monsoon rains. These environmental changes influence the durations of growing seasons and the number of crops per year, resulting in changes in productivity. To validate the performance of the model, we compared model estimates for the heading date and changes in leaf area index at nine sites with estimates of these parameters derived from MODIS satellite time series data for the period 2002-2006. The root mean square errors of heading date between the modeled and satellite data in the upper, middle, and coastal regions of the delta were 17.6, 11.2, and 13.0 days, respectively. Based on the model, we examined case studies to assess the changes in cropping cycles and crop failures in the VMD due to extreme flooding in 2000 and salinity intrusion in 2004 by applying evaluation indices defined by available period for cultivation (APC) and safe margin for cropping (SMC) which is defined as the marginal time between APC and the period required for cultivation. Findings of case studies suggested that a small difference in the SMC of the cropping pattern is critical to the stability and productive capacity of the rice crop.

Rice productivity tends to become similar among neighboring regions because of spillover effects, but few previous studies considered such spatial dependence in Japanese rice production. Measuring such spatial influences is important for policy making. This study analyzed the socio-economic and climate factors for rice total factor productivity （TFP） by considering spatial dependence caused by technological spillover and other latent factors.<br>Results of the spatial auto-regressive （SAR） model and spatial error model （SEM） showed the following. First, effects of spatial dependence in rice TFP level remained even when climate factors, which affect TFP in broader regions than prefectural areas, were controlled. Hence, some biases occur in ordinary least square estimations commonly used in the panel data analysis. Second, the elasticity of TFP with respect to temperature via rice yield was positive until 2000 in most prefectures where summer temperatures were under the threshold level to cause changes in yield. The elasticity of TFP was high and remained positive until the 2050's in northern Japan. However, in middle and western regions and Kyushu, the elasticity was predicted to become negative after 2030. The same tendency was found in TFP elasticity for effects of temperature on rice quality. The threshold value for temperature in rice quality was lower than rice yield, so an increase in temperature depresses TFP in middle and western regions and Kyushu even under the present situations. Only a few prefectures in northern Japan can achieve an increase in TFP at present. The TFP elasticity against rainfall is constantly negative. Impacts of long rain were stronger than floods. As shown by these tendencies in TFP elasticity, future climate change negatively affects rice TFP via temperature and rain. Third, the ratios of indirect effects from neighboring prefectures by causative factors against total effects on TFP changes, measured by the SAR model, were high in northern Japan where neighbor TFP levels were relatively high. Contrarily, the ratio was low in middle-and-western Japan where neighbor TFP levels were low. Even so, the percentage of indirect effects compared with total effects was approximately 10 to 25% in all regions and throughout all periods.<br>As such, spatial econometric models are useful to show results without spatial biases. Based on these results, technological development of rice production and provision of precise climate prediction to farmers are important to mitigate the influences of climate change.<br><br>JEL Classification:D24, O30, Q11, Q16, R32

The consequences of changes observed in climate and management to yield trends in major crop-producing regions have implications for future food availability. We present an assessment of the impacts of historical changes in sowing date and climate to the maize yield trend in the U.S. Corn Belt from 1980 to 2006 by using large-area crop modeling and a data assimilation technique (i.e., the model optimization based on the Markov chain Monte Carlo method). Calibrated at a regional scale, the model captured the major characteristics of the changes reported in yield as well as the timing and length of maize growth periods across the Corn Belt. The simulation results using the calibrated model indicate that while the climate change observed for the period likely contributed to a decreasing yield trend, the positive contribution from the reported shift to an earlier sowing date offset the negative impacts. With the given spread in the assessment results across previous studies and in this study, the conclusion that the negative impacts of climate change on U.S. maize yield trend more likely derive from a decreasing trend in growing-season precipitation than to an increasing trend in temperature.

The monitoring and prediction of climate-induced variations in crop yields, production and export prices in major food-producing regions have become important to enable national governments in import-dependent countries to ensure supplies of affordable food for consumers. Although the El Nino/Southern Oscillation (ENSO) often affects seasonal temperature and precipitation, and thus crop yields in many regions, the overall impacts of ENSO on global yields are uncertain. Here we present a global map of the impacts of ENSO on the yields of major crops and quantify its impacts on their global-mean yield anomalies. Results show that El Nino likely improves the global-mean soybean yield by 2.1-5.4% but appears to change the yields of maize, rice and wheat by - 4.3 to +0.8%. The global-mean yields of all four crops during La Nina years tend to be below normal (- 4.5 to 0.0%). Our findings highlight the importance of ENSO to global crop production.

Understanding the effects of climate change is vital for food security. Among the most important environmental impacts of climate change is the direct effect of increased atmospheric carbon dioxide concentration ([CO2]) on crop yields, known as the CO2 fertilization effect. Although several statistical studies have estimated past impacts of temperature and precipitation on crop yield at regional scales, the impact of past CO2 fertilization is not well known. We evaluated how soybean yields have been enhanced by historical atmospheric [CO2] increases in three major soybean-producing countries. The estimated average yields during 2002-2006 in the USA, Brazil, and China were 4.34%, 7.57%, and 5.10% larger, respectively, than the average yields estimated using the atmospheric [CO2] of 1980. Our results demonstrate the importance of considering atmospheric [CO2] increases in evaluations of the past effects of climate change on crop yields.

AimRecent changes in crop yields have implications for future global food security, which are likely to be affected by climate change. We developed a spatially explicit global dataset of historical yields for maize, soybean, rice and wheat to explore the historical changes in mean, year-to-year variation and annual rate of change in yields for the period 1982-2006.
LocationThis study was conducted at the global scale.
MethodsWe modelled historical and spatial patterns of yields at a grid size of 1.125 degrees by combining global agricultural datasets related to the crop calendar and harvested area in 2000, country yield statistics and satellite-derived net primary production. Modelled yields were compared with other global datasets of yields in 2000 (M3-Crops and MapSPAM) and subnational yield statistics for 23 major crop-producing countries. Historical changes in modelled yields were then examined.
ResultsModelled yields explained 45-81% of the spatial variation of yields in 2000 from M3-Crops and MapSPAM, with root-mean-square errors of 0.5-1.8 t ha(-1). Most correlation coefficients between modelled yield time series and subnational yield statistics for the period 1982-2006 in major crop-producing regions were greater than 0.8. Our analysis corroborated the incidence of reported yield stagnations and collapses and showed that low and mid latitudes in the Southern Hemisphere (0-40 degrees S) experienced significantly increased year-to-year variation in maize, rice and wheat yields in 1994-2006 compared with that in 1982-93.
Main conclusionsOur analyses revealed increased instability of yields across a broad region of the Southern Hemisphere, where many developing countries are located. Such changes are likely to be related to recent yield stagnation and collapses. Although our understanding of the impacts of recent climate change, particularly the incidence of climate extremes, on crop yields remains limited, our dataset offers opportunities to close parts of this knowledge gap.

This study aimed to (1) develop a crop-water coupled model that can simultaneously simulate crop growth and yield over a river basin, river discharge and their interactions by embedding a large-scale crop model, PRYSBI-2 (Sakurai et al., 2014) into a global water resources model, H08 (Hanasaki et al., 2008); (2) apply the developed couple model to Northeast China and evaluate the model&rsquo;s reproducibility by comparing the model simulated soybean yield, river discharge, soil moisture, crop actual evapotranspiration in the historical past, with the observations; (3) assess climate change impact on soybean production and balance between crop water supply and demand with multi- climate change scenarios and irrigation-based adaptation scenarios.

This study is a global assessment of the performance of two empirical models for estimating daily global solar radiation (SR) in the present-day climate, as well as the applicability of these models for near-future climates. One empirical model is used to estimate SR from a diurnal temperature range (DTR) and is called the DTR-SR model, while the other is used to estimate SR from relative humidity (RH) and is called the RH-SR model. Using data from the reanalysis and atmosphere-ocean coupled global climate model (GCM) known as MIROC4, we found that the DTR-SR model operates more accurately over land than the RH-SR model, though the RH-SR model outperformed the DTR-SR model over the ocean. Based on an assessment using data from the decadal prediction performed by the GCM, the DTR-SR model could be applied to estimate GCM-simulated SR in altered climates until the year 2035 considered in this study, given the comparatively limited performance of the model during the winter and in arid and semi-arid regions. This suggests that the DTR-SR model is likely useful for data imputation if GCM-simulated SR data are unavailable.

Consumers, including the poor in many countries, are increasingly dependent on food imports(1) and are thus exposed to variations in yields, production and export prices in the major food-producing regions of the world. National governments and commercial entities are therefore paying increased attention to the cropping forecasts of important food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years(2-4). Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We found that moderate-to-marked yield loss over a substantial percentage (26-33%) of the harvested area of these crops is reliably predictable if climatic forecasts are near perfect. However, only rice and wheat production are reliably predictable at three months before the harvest using within-season hindcasts. The reliabilities of estimates varied substantially by crop-rice and wheat yields were the most predictable, followed by soybean and maize. The reasons for variation in the reliability of the estimates included the differences in crop sensitivity to the climate and the technology used by the crop-producing regions. Our findings reveal that the use of seasonal climatic forecasts to predict crop failures will be useful for monitoring global food production and will encourage the adaptation of food systems to climatic extremes.

At the Takayama deciduous broadleaved forest Asiaflux site in Japan, the ecosystem carbon dynamics have been studied for more than two decades. In 2005, we installed non-dispersive infrared CO2 sensors in the soil below the site's flux tower to systematically study vertical soil-air CO2 dynamics and explain the behavior of soil surface CO2 efflux. Soil-air CO2 concentrations measured from June 2005 through May 2006 showed sinusoidal variation, with maxima in July and minima in winter, similar to the soil CO2 effluxes measured simultaneously using open-flow chambers. Soil-air CO2 concentrations increased with soil depth from 5 to 50 cm: from 2,000 to 8,000 ppm in the summer and from 2,000 to 3,000 ppm in the winter under snow. Summer soil-air CO2 concentrations were positively correlated with soil moisture on daily and weekly scales, indicating that the Oi, Oe, and A horizons, where decomposition is accelerated by high-moisture conditions, contributed substantially to CO2 emissions. This result is consistent with the short residence time (about 2 h) of CO2 in the soil and larger emissions in shallow soil layers based on our diffusion model. We revealed for the first time that soil-air CO2 concentrations in winter were correlated with both snow depth and wind speed. CO2 transfer through the snow was hundreds of times the gas diffusion rates in the soil. Our estimate of the CO2 efflux during the snow-cover season was larger than previous estimates at TKY, and confirmed the important contribution of the snow-cover season to the site's carbon dynamics.

The grid interval of a global climate model (GCM) is generally hundreds of kilometers in latitude and longitude. The spatial heterogeneity of crop condition (e.g., phenology and yields) at that scale could be substantial. Because the atmosphere-cropland exchanges of energy, water, and materials are sensitive to crop condition, this issue poses a question: How can we simulate the condition of a crop of interest on a GCM grid scale while taking into account the spatial heterogeneity of crop condition at a sub-grid scale? We therefore proposed an ensemble approach that uses stochastic parameter values to represent the spatial variation in the phenological and biophysical characteristics of a crop within a given GCM grid box, and tested its feasibility with simulation experiments. The combination of the Soil and Water Assessment Tool (SWAT) applied to maize in the Central Great Plains, United States, and coarse-resolution (2.5 degrees x2.5 degrees) reanalysis data was taken as the example. The ensemble simulations successfully captured the spatial variation in the phenology and yield. Our conclusion is that the ensemble approach is feasible and expected to benefit large-area crop modeling when extending those models to include more information on the spatial heterogeneity of crop condition than ever.

We evaluated the impacts of historical land-use changes (LUCs) from 1987 to 2006 on surface warming rates and rice yields on the island of Shikoku, Japan. We performed two types of numerical simulations (with historical LUCs and with fixed land uses) using a regional atmospheric model (JMA-NHM) and a large-area rice-growth model (PRYSBI). During our study period in Shikoku, the area of paddy fields decreased markedly and the area of building lots and roads increased. Our evaluation suggests that these LUCs caused warming rates in and around paddy fields that were five times those in and around other land uses. The simulated rice yield in 2006 was 0.27% lower (0.012 t ha(-1)) than in 1987 in response to the change in thermal and solar radiation conditions. These results suggest that decreases of crop yield due to environmental deterioration will be found in other regions where similar LUCs are occurring. Citation: Yoshida, R., T. Iizumi, M. Nishimori, and M. Yokozawa (2012), Impacts of land-use changes on surface warming rates and rice yield in Shikoku, western Japan, Geophys. Res. Lett., 39, L22401, doi:10.1029/2012GL053711.

To evaluate annual CO2 exchange rates in a wetland ecosystem, ecosystem respiration rate (R-e), net ecosystem productivity (NEP), and gross primary productivity (GPP) were investigated using the Closed Geosphere Experiment Facility (CGEF) located in northeastern Japan. The CGEF is highly airtight and equipped with a Geosphere Module (GM). The GM has a ground area of 5.8 x 8.7 m(2) and an average height of 11.9 m, including a soil depth of 3.1 m. A wetland ecosystem dominated by Phragmites australis was introduced into the CGEF. Air temperature and CO2 concentration in the GM were controlled automatically. The hourly nighttime R-e increased exponentially with the hourly average air temperature. Both hourly NEP and GPP depended on hourly photosynthetic photon flux density (PPFD). In addition, daily ecosystem CO2 exchange rates (R-e, NEP, and GPP) were influenced by above-ground plant biomass. The annual NEP was found to be 64.2 +/- 19.2 g C m(-2) yr(-1) and it resulted from the annual GPP of 555.8 +/- 17.0 g C m(-2) yr(-1) and annual R-e of -491.6 +/- 15.6 g C m(-2) yr(-1). Therefore, the wetland ecosystem behaved as a CO2 sink for the entire year. The annual CO2 exchange rates obtained were reasonable values compared to the findings of published studies in P. australis-dominated wild wetlands using the eddy covariance technique and the combined method of internal gas pressures and flow measurements and harvesting.

Change in temperature sensitivity of soil organic carbon (SOC) decomposition with change in soil qualities (i.e. decomposability or lability) is one of the most important issues to be evaluated for projection of future CO2 emissions from soils. We inversely estimated the temperature sensitivity of SOC decomposition rate by applying a hybrid of the Metropolis-Hasting algorithm and the particle filter method to the extended Rothamsted carbon model (RothC), together with long-term (9 years) experimental data on SOC obtained at five sites in Japanese upland soils. Contrary to the prediction of the Arrhenius kinetics theory, we found no significant differences in temperature sensitivity among soils with different qualities (represented as soil compartments in the RothC model). We also confirmed that there was a positive correlation between the relative temperature sensitivity of the humus compartment and future total CO2 emissions. The RothC model with default parameterization tended to overestimate future total CO2 emissions relative to the calibrated model, and the degree of overestimation was larger than that of underestimation. (C) 2011 Elsevier Ltd. All rights reserved.

Daily global solar radiation (SR) is one of essential weather inputs for crop, hydrological, and other simulation models to calculate biomass production and potential evapotranspiration. The availability of long-term observed SR data is, however, limited, especially in developing countries. This hinders climate applications in various sectors in these countries. To overcome this difficulty, we here propose a method to infer the reasonable daily SR condition for past decades from global reanalysis and limited observed SR data. The method consists of the regression-based statistical downscaling method and two empirical models for estimating the SR condition (i.e. the S-model and the DTR/RH-model). These empirical models were independent in terms of the variables explaining the SR condition. The regression models were trained on the basis of the SR conditions estimated by the S-model and the DTR/RH-model instead of the observed SR data. The Markov Chain Monte Carlo (MCMC) technique was applied to determine the parameter values of these models that guide the models to provide SR conditions that are close in value to each other at both the site and domain-mean scales. After that, we computed the SR condition over the 30 years from 1978 through 2007 at 17 sites in the Vietnam Mekong Delta area using the determined parameter values. The inferred SR condition was close in value to the corresponding observations available from the literature. This suggests that the proposed method yielded a reasonable inference of the SR condition at the sites despite the limited availability of observed SR data. The provided estimates of the daily SR condition over the past 30 years are useful for climate applications in agricultural, hydrological, and other sectors in this area. Copyright (c) 2011 Royal Meteorological Society

A method for estimating the area of crop damage due to tropical cyclones (TCs) by using fragility curves (FCs) is proposed. FCs, which represent the probability of damage caused by external forces, are one method considered appropriate for estimating structural damage caused by natural disasters. Here, FCs are applied to estimate the area of damage to paddy rice resulting from typhoons in Japan. The FCs for paddy rice are assumed to vary with growth stage. Statistical data on areas damaged by 42 typhoons that have struck Japan between 1991 and 2007, together with observed meteorological data, are used to derive the FCs. In general, our estimates agree with the reported areas of damage for the 42 typhoons, especially for typhoons that affected large areas of paddy rice. Moreover, from statistical data on crop damage due to typhoons, the proposed method successfully shows that the heading stage of paddy rice is the stage most vulnerable to typhoons, as found in earlier experimental studies and post-disaster investigations.

This study examined the impacts of climate change both on rice quality and yield using a climatologically empirical rice quality model together with a process-based rice growth model. The target area is Kyushu in the western part of Japan, where the decline in rice quality already occurs at the present time. After confirming the model reproducibility, we made simulations to project the changes of rice quality under climate change scenarios. All the projections suggested that the rice quality would significantly deteriorate. We analyzed the results by distinguishing the direct and indirect effects of climate based on simulations with changing weather data. It suggested that the indirect effect induced by the shift of the grain-filling period is relatively larger than the direct effect by temperature rise. We also examined the effects of adaptation measure shifting the date of transplanting on rice quality and yield. It is shown that rice quality is more sensitive to the shifting transplanting date than rice yield
consequently, shifting the transplanting date later might be an appropriate measure for the adaptation to climate change. © 2011, The Society of Agricultural Meteorology of Japan. All rights reserved.

Projecting the impacts of climate change includes various uncertainties from physical, biophysical, and socioeconomic processes. Providing a more comprehensive impact projection that better represents the uncertainties is a priority research issue. We used an ensemble-based projection approach that accounts for the uncertainties in climate projections associated with general circulation models (GCMs) and biophysical and empirical parameter values in a crop model. We applied the approach to address the paddy rice yield change in Japan in the 2050s (2046-2065) and 2090s (2081-2100) relative to the 1990s (1981-2000). Seventeen climate projections, nine (eight) climate projections performed by seven (six) GCMs conditional on the Special Report on Emission Scenarios (SRES) A1B (A2), were included in this projection. In addition, 50 sets of biophysical and empirical parameter values of a large-scale process-based crop model for irrigated paddy rice were included to represent the uncertainties of crop parameter values. The planting windows, cultivation practices, and crop cultivars in the future were assumed to be the same as the level in the baseline period (1990s). The resulting probability density functions conditioned on SRES A1B and A2 indicate projected median yield changes of + 17.2% and + 26.9% in Hokkaido, the northern part of Japan, in the 2050s and 2090s with 90% probability intervals of (-aEuro parts per thousand 5.2%, + 40.3%) and (+ 6.3%, + 51.2%), relative to the 1990s mean yield, respectively. The corresponding values in Aichi, on the Pacific side of Western Japan, are 2.2% and -aEuro parts per thousand 0.8%, with 90% probability intervals of (-aEuro parts per thousand 15.0%, + 14.9%) and (-aEuro parts per thousand 33.4%, + 17.9%), respectively. We also provided geographical maps of the probability that the future 20-year mean yield will decrease and that the future standard deviation of yield for 20 years will increase. Finally, we investigated the relative contributions of the climate projection and crop parameter values to the uncertainty in projecting yield change in the 2090s. The choice of GCM yielded a relatively larger spread of projected yield changes than that of the other factors. The choice of crop parameter values could be more important than that of GCM in a specific prefecture.

Ongoing climate change is likely to enhance the deterioration of rice quality that has been observed in western Japan, especially in Kyushu, since the 1990s. Therefore, it is important to examine the response of rice quality to environmental variation over a wide geographical domain. To that end, the aims of this study were (i) to propose a statistical model to predict rice quality based on temperature, total radiation during the ripening period, and their multiple effects; and (ii) to evaluate the model validity and uncertainty in prediction. A Bayesian calibration was adopted to account for uncertainty in the parameter values associated with non-climatic factors. The validation results showed that the model performed well in capturing the temporal trend and interannual variation in observed rice quality in all prefectures of Kyushu. We then performed the prediction experiment for rice quality in the extremely hot summer of the year 2010, which was omitted from the model calibration data. The results showed that the predictive capability of the statistical model is somewhat dependent on the calibration data, but this dependency does not necessarily mean that useful predictions for climates not in the calibration data are impossible.

There are increasing attempts to define the measures of &apos;dangerous anthropogenic inference with the climate system&apos; in context of Article 2 of the Framework Convention on Climate Change, due to its linkage to goals for stabilizing greenhouse gas concentrations. The criteria for identifying dangerous anthropogenic interference may be characterized in terms of the consequences of climate change. In this study, we use the water stress index (WSI) and agricultural net primary production (NPP) as indictors to assess where and when there might be dangerous effects arising from the projected climate changes for Chinese agricultural production. The results showed that based on HadCM3-based climate change scenarios, the region between the North China Plain and Northeast China Plain (34.25-47.75 degrees N, 110.25-126.25 degrees E) would be vulnerable to the projected climate change. The analyses on inter-annual variability showed that the agricultural water resources conditions would fluctuate through the period of 2001-2080 in the region under IPCC SRES A2 scenario, with the period of 2021-2040 as critical drought period. Agricultural NPP is projected to have a general increasing trend through the period of 2001-2080; however, it could decrease during the period of 2005-2035 in the region under the IPCC SRES A2 scenario, and during the period of 2025-2035 under IPCC SRES B2 scenario. Generally, while projected climate change could bring some potentially improved conditions for Chinese agriculture, it could also bring some critical adverse changes in water resources, which would affect the overall outcome. At this stage, while we have identified certain risks and established the general shape of the damage curve expressed as a function of global mean temperature increase, more works are needed to identify specific changes which could be dangerous for food security in China. Therefore, there is a need for the development of more integrated assessment models, which include social-economic, agricultural production and food trade modules, to help identify thresholds for impacts in further studies.

In this study, we evaluate the accuracy of four regional climate models (NHRCM, NRAMS, TRAMS, and TWRF) and one bias correction-type statistical model (CDFDM) for daily precipitation indices under the present-day climate (1985-2004) over Japan on a 20 km grid interval. The evaluated indices are (1) mean precipitation, (2) number of days with precipitation 1 mm/d (corresponds to number of wet days), (3) mean amount per wet day, (4) 90th percentile of daily precipitation, and (5) number of days with precipitation 90th percentile of daily precipitation. The boundary conditions of the dynamical models and the predictors of the statistical model are given from the single reanalysis data, i.e., JRA25. Both types of models successfully improved the accuracy of the indices relative to the reanalysis data in terms of bias, seasonal cycle, geographical pattern, cumulative distribution function of wet-day amount, and interannual variation pattern. In most aspects, NHRCM is the best model of all indices. Through the intercomparison between the dynamical and statistical models, respective strengths and weaknesses emerged. Briefly, (1) many dynamical models simulate too many wet days with a small amount of precipitation in humid climate zones, such as summer in Japan, relative to the statistical model, unless the cumulus convection scheme improved for such a condition is incorporated
(2) a few dynamical models can derive a better high-order percentile of daily precipitation (e.g., 90th percentile) than the statistical model
(3) both the dynamical and statistical models are still insufficient in the representation of the interannual variation pattern of the number of days with precipitation 90th percentile of daily precipitation
(4) the statistical model is comparable to the dynamical models in the long-term mean geographical pattern of the indices even on a 20 km grid interval if a dense observation network is applicable
(5) the statistical model is less accurate than the dynamical models in the temporal variation pattern due to the strong dependence of the predictand on the relatively less accurate predictor (daily reanalysis precipitation)
and (6) the simple statistical model is less plausible in the physical sense because of the oversimplification of underlying physical processes compared to the dynamical models and more sophisticated statistical models. Copyright 2011 by the American Geophysical Union.

Eddy covariance (EC) flux measurement is the most-used technique for observation of the fluxes of sensible heat, latent heat, and carbon dioxide between the land surface and the atmospheric boundary layer. Despite the availability of plentiful EC data from numerous research projects, it is difficult to make meaningful comparisons of EC at different sites, to validate the models used, and to integrate observed data with models because the uncertainties of the method are inadequately defined. We developed a method to evaluate the uncertainties of the EC method without the need to consider individual site specifications and flux characteristics. We showed that the fractional error φ of EC (i.e., tolerance T) can be separated into random and illegitimate components. T can be used as a scale parameter for spatiotemporal stationarity, and can be defined as a rational function. We demonstrated a practical application of T analyses for two contrasting areas: a low-relief paddy field and an area of more complex land-forms where dramatic wind changes affect fluxes, and showed that T analysis provides an appropriate and effective method to determine the uncertainties in EC.<br>

The relationship between climatic factors and crop yields for maize and soybeans in 3 major producing countries (the United States, Brazil, and China) was analyzed statistically. Temporal changes in the climate-yield relationship were considered, and the temporal and spatial variations were evaluated. County-level data from 1980 to 2006 were collected for each country and allocated to 1.125 x 1.125 grids. Data were analyzed for temporal changes in the effect of climate on yields using the particle filtering method for each grid. The 'current' effect of temperature on crop yields was shown to be geographically symmetric around the optimal temperatures (19.51 C for maize and 20.66 C for soybeans) in the Northern Hemisphere. In the Southern Hemisphere, the 'current' effect of precipitation was more influential than that of temperature. The effects of these climatic factors changed over time. Whereas the negative effect of high temperatures has been mitigated around the corn belt of the United States during the last 3 decades, it has intensified in Brazil, northern China, and the southern United States for maize. Consequently, predicted future yields differed marginally depending on the relationship (past or current effect) used for the prediction, even when they were summarized on country scales. This study suggests that temporal changes in the relationship between weather and crop yields should be considered for better future predictions.

In this study, we evaluate the accuracy of four regional climate models (NHRCM, NRAMS, TRAMS, and TWRF) and one bias correction-type statistical model (CDFDM) for daily precipitation indices under the present-day climate (1985-2004) over Japan on a 20 km grid interval. The evaluated indices are (1) mean precipitation, (2) number of days with precipitation &gt;= 1 mm/d (corresponds to number of wet days), (3) mean amount per wet day, (4) 90th percentile of daily precipitation, and (5) number of days with precipitation &gt;= 90th percentile of daily precipitation. The boundary conditions of the dynamical models and the predictors of the statistical model are given from the single reanalysis data, i.e., JRA25. Both types of models successfully improved the accuracy of the indices relative to the reanalysis data in terms of bias, seasonal cycle, geographical pattern, cumulative distribution function of wet-day amount, and interannual variation pattern. In most aspects, NHRCM is the best model of all indices. Through the intercomparison between the dynamical and statistical models, respective strengths and weaknesses emerged. Briefly, (1) many dynamical models simulate too many wet days with a small amount of precipitation in humid climate zones, such as summer in Japan, relative to the statistical model, unless the cumulus convection scheme improved for such a condition is incorporated; (2) a few dynamical models can derive a better high-order percentile of daily precipitation (e.g., 90th percentile) than the statistical model; (3) both the dynamical and statistical models are still insufficient in the representation of the interannual variation pattern of the number of days with precipitation &gt;= 90th percentile of daily precipitation; (4) the statistical model is comparable to the dynamical models in the long-term mean geographical pattern of the indices even on a 20 km grid interval if a dense observation network is applicable; (5) the statistical model is less accurate than the dynamical models in the temporal variation pattern due to the strong dependence of the predictand on the relatively less accurate predictor (daily reanalysis precipitation); and (6) the simple statistical model is less plausible in the physical sense because of the oversimplification of underlying physical processes compared to the dynamical models and more sophisticated statistical models.

The present paper reports how stand size-structure dynamics due to competition between differentsized trees affect long-term forested water balance in Japanese cool-temperate planted stands (evergreen coniferous Cryptomeria japonica and deciduous coniferous Larix kaempferi stands) using a fully coupled multi-layered meteorological surface physics-terrestrial ecosystems model. The simulation captured the well-known annual variation in leaf area index (LAI) accurately with stand age in monocultured and even-aged stands; the occurrence of maximum LAI during the early growth stage and then a gradual decline followed by a steady state after the maximum LAI. The simulations also detected a high dependency of annual evapotranspiration (AETr) on LAI with stand age that is well known by prior observational researches. In the C. japonica (shade-tolerant late-successional species) stand, the relationship between annual net primary productivity of an individual tree (NPPind) and individual tree mass (w) changed from linear to a convex curve during self-thinning, indicating that the degree of asymmetric tree competition intensified with forest stand development. The higher degree of competitive asymmetry characterized by the convex-shaped NPPind-w relationship produced greater size inequality, i.e., the formation of trees stratified by height. Under such conditions. AETr and annual transpiration (ATr) were mainly regulated by larger trees. On the other hand, the NPPind-w relationships in the L. kaempferi (shade-intolerant early-successional species) stand were linear throughout the simulated period, indicating the lower degree of competitive asymmetry. Under such conditions, the growth of intermediate-sized trees was enhanced and these trees became a dominant source of AETr (and also ATr) during self-thinning. Furthermore, the sensitivity analysis of the effects of ecophysiological parameters such as foliage profile (i.e., vertical distribution of leaf area density) of an individual tree (distribution pattern is described by the parameter eta), the maximum carboxylation velocity (V-cmaxo) and biomass allocation pattern of individual plant growth (mu(1)) on AETr, ATr and annual runoff (AR(off)) showed that the temporal trends of AETr, ATr, ARoff and NPPind-w relationships were completely the same as those in the control simulations. However, the NPPind-w relationship during self-thinning indicated higher degrees of competitive asymmetry when eta or Vcmaxo were greater than those in the control simulation and generated greater AETr and ATr and thus smaller AR(off). We found that more asymmetric tree competition brings about greater size inequality between different-sized trees and thus more evapotranspiration and less runoff in a forest stand. Overall, our simulation approach revealed that not only LAI dynamics but also plant competition, and thus size-structure dynamics, in a forest ecosystem are essential to long-term future projections of forested water balance.(C) 2010 Elsevier B.V. All rights reserved.

This article outlined the statistical downscaling methods (SDMs) and how they derive climatic inputs for the impact models (referred to as the climate change scenario) from the outputs of dynamic global/regional climate models. To help select an appropriate method from various SDMs, the authors categorizes the SDMs into four functional categories, i.e. (1) temporal disaggregation, (2) spatial disaggregation, (3) estimation of elements not directly supplied by climate models, and (4) bias correction, and referred to some anecdotal studies of each category. In addition, a practical example of the generation of climate change scenario at a site was demonstrated to provide a concrete image for researchers trying to use SDMs. This introductory guide will help select an appropriate SDM to fill the gap between accessible climate model outputs and the requirements of impact studies. © 2010, The Society of Agricultural Meteorology of Japan. All rights reserved.

This study quantifies the ranges of climate model biases in surface air temperature for July and August (summer temperature) and daily total insolation for May-October (warm-season insolation) that can give simulated regional paddy rice yields with a bias within +/- 2.5% of the 20-yr mean observed regional yield. The following four sets of three meteorological elements ( daily maximum and minimum temperatures and daily total insolation) from daily climate model outputs were used as meteorological inputs for a large-scale crop model for irrigated paddy rice: 1) raw climate model outputs of all meteorological elements, 2) bias-corrected temperatures and raw climate model outputs of insolation, 3) bias-corrected insolation and raw climate model outputs of temperatures, and 4) bias-corrected climate model outputs of all meteorological elements. These meteorological inputs were sourced from seven coupled general circulation models, one regional climate model, and one reanalysis dataset. Crop model simulations with artificially biased meteorological inputs were also used. By using the approximation formula derived from these crop model simulation results and the Monte Carlo simulation technique, it was found that climate model outputs with biases within +/- 0.6 degrees C and +/- 3% for summer temperature and warm-season insolation, respectively, could result in a simulated regional paddy rice yield with a bias within +/- 2.5% of the 20-yr mean observed regional yield. The simulated regional yield was less biased not only when the biases of two meteorological inputs were small but also when the cold or warm bias of summer temperature and the overestimation of warm-season insolation were balanced through the crop model processes. The methodology presented here will lead to a better and more comprehensive understanding of the nature of error propagation from a climate model to an application model and will facilitate the selection of climate models suitable for specific applications.

We estimated the carbon (C) sequestration potential of organic matter application in Japanese arable soils at a country scale by applying the Rothamsted carbon (RothC) model at a 1-km resolution. After establishing the baseline soil organic carbon (SOC) content for 1990, a 25-year simulation was run for four management scenarios: A (minimum organic matter application), B (farmyard manure application), C (double cropping for paddy fields) and D (both B and C). The total SOC decreased during the simulation in all four scenarios because the C input in all four scenarios was lower than that required to maintain the baseline 1990 SOC level. Scenario A resulted in the greatest depletion, reflecting the effects of increased organic matter application in the other scenarios. The 25-year difference in SOC accumulation between scenario A and scenarios B, C and D was 32.3, 11.1 and 43.4 Mt C, respectively. The annual SOC accumulation per unit area was similar to a previous estimate, and the 25-year averages were 0.30, 0.10 and 0.41 t C ha-1 year-1 for scenarios B, C and D, respectively. The system we developed in the present study, that is, linking the RothC model and soil spatial data, can be useful for estimating the potential C sequestration resulting from an increase in organic matter input to Japanese arable soils, although more feasible scenarios need to be developed to enable more realistic estimation.

A numerical hydraulic model was developed and used to investigate the spread of salinity intrusion and the propagation of flooding in the Vietnamese Mekong Delta under two proposed scenarios: "baseline" (2000s) and "future" (2090s). The baseline scenario was based on observed hydrological and salinity intrusion data from 1998 to 2006. The changes in upstream flow discharge in the future scenario (increased for most of the year) were derived from previous research results obtained using the Japan Meteorological Agency atmospheric general circulation model output for the IPCC SRES A1B scenario. The sea level rise in the A1B scenario (a projected 53-cm increase) was also included in the future scenario. The resulting salinity intrusion and flood water levels were then used to roughly estimate possible rice cropping durations. We found that large adverse changes in duration of rice cultivation in the future scenario were caused mainly by floods with greater peaks, larger areal extents, and longer durations. The area potentially suitable for growing triple rice crops decreased from 31% of the total delta area currently to 5%, whereas that potentially suitable for a single rice crop increased from 21% currently to 62%. Using GIS techniques, we divided the delta into three areas with different levels of rice vulnerability levels. Areas of high and medium vulnerability covered approximately 31 and 36% of the total delta area, respectively. © 2010, The Society of Agricultural Meteorology of Japan. All rights reserved.

This study examined the potential predictability of paddy rice yield variation on a local scale (approximately 2 km × 2 km) by using a prefectural-scale dynamic paddy rice simulation model (the PRYSBI model) with the local observed weather data, taking the 160 local areas in Tochigi Prefecture, Japan, as the study area. From the comparison of the simulated and observed local yields during the 11-year period (1993, 1995, 1998-2006), the PRYSBI model showed high capability to simulate the interannual variation of area-mean local yield over the study area with the quite large root-mean-square error (RMSE) of 2.1 Mg ha^-1. However, the RMSE has the statistical-significant relationship with the local areal features on agriculture. We thereby incorporated the local areal features in the simulated yields in the manner of the multiplicative model approach. The parameter values of the multiplicative model were estimated by using the Bayesian approach, which can count the uncertainty of parameter value in a stochastic manner. By this means, the potential predictability in terms of the coefficient of determination (r²) and RMSE between the simulated and observed local yields improved from r²=0.430 to r²=0.527 and from RMSE=2.0 Mg ha^-1 to RMSE=0.4 Mg ha^-1 compared to the PRYSBI model alone. The potential predictability of local yield could improve by incorporating the local areal features in the output of crop model.<br>

A simulation study was carried out to investigate simultaneously the effects of eco-physiological parameters on competitive asymmetry, self-thinning, stand biomass and NPP in a temperate forest using an atmosphere-vegetation dynamics interactive model (MINoSGI). In this study, we selected three eco-physiological relevant parameters as foliage profiles (i.e. vertical distribution of leaf area density) of individual trees (distribution pattern is described by the parameter eta), biomass allocation pattern in individual tree growth (chi) and the maximum carboxylation velocity (V(max)) The position of the maximal leaf area density shifts upward in the canopy with increasing eta. For scenarios with eta 4 (foliage concentrated in the lowest canopy layer) or eta &gt; 12 (foliage concentrated in the uppermost canopy layer), a low degree of competitive asymmetry was produced. These scenarios resulted in the survival of subordinate trees due to a brighter lower canopy environment when eta 4 or the generation of spatially separated foliage profiles between dominant and subordinate trees when eta &gt; 12. In contrast, competition between trees was most asymmetric when 4 &lt;= eta &lt;= 12 (vertically widespread foliage profile in the canopy), especially when eta = 8. In such cases, vertically widespread foliage of dominant trees lowered the opportunity of light acquisition for subordinate trees and reduced their carbon gain. The resulting reduction in carbon gain of subordinate trees yielded a higher degree of competitive asymmetry and ultimately higher mortality of subordinate trees. It was also shown that 4 &lt;= eta &lt;= 12 generated higher self-thinning speed, smaller accumulated NPP, litter-fall and potential stand biomass as compared with the scenarios with eta 4 or eta &gt; 12. In contrast, our simulation revealed small effects of chi or V(max) on the above-mentioned variables as compared with those of eta. In particular, it is notable that greater V(max) would not produce greater potential stand biomass and accumulated NPP although it has been thought that physiological parameters relevant to photosynthesis such as V(max) influence dynamic changes in forest stand biomass and NPP (e.g. the greater the V(max), the greater the NPP). Overall, it is suggested that foliage profiles rather than biomass allocation or maximum carboxylation velocity greatly govern forest dynamics, stand biomass, NPP and litter-fall. (C) 2009 Elsevier B.V. All rights reserved.

Estimates of climate change impacts are plague with uncertainties from many physical, biological, and social-economic processes. Among the urgent research priorities, more comprehensive assessments of impacts that better represent the uncertainties are needed. Here, we develop a new super-ensemble-based probabilistic projection approach to account for the uncertainties from CO(2) emission scenarios, climate change scenarios, and biophysical processes in impact assessment model. We demonstrate the approach in addressing the probabilistic changes of maize production in the North China Plain in future. The new process-based general crop model, MCWLA [Tao, F., Yokozawa, M. Zhang, Z., 2009. Modelling the impacts of weather and climate variability on crop productivity over a large area: a new process-based model development, optimization, and uncertainties analysis. Agric. For. Meteorol. 149,831-850], is used. MCWLA accounts for the key impact mechanisms of climate variability and is accurate over a large area. We use 10 climate scenarios consisting of the combinations of five GCMs and two emission scenarios, the corresponding atmospheric CO(2) concentration range, and 60 sets of crop model parameters representing the biophysical uncertainties from crop models. The planting window is set to allow planting shift within the window, although the crop cultivar and management practice in future were assumed to be same as the level during the baseline period. The resulting probability distributions indicate expected yield changes of -9.7, -15.7, -24.7% across the maize cultivation grids in Henan province during 2020s, 2050s, and 2080s, with 95% probability intervals of (-29.4,+15.8).(-45.7,+24.0), (-92.8, +20.3) in percent of 1961-1990 yields, respectively. The corresponding value in Shandong province is -9.1, -19.0. -25.5%, with 95% probability intervals of (-28.4, +17.4),(-45.4, +15.9),(-60.1, +12.8). We also investigate the temporal and spatial pattern of changes and variability in maize yield across the region. Besides the new findings on the probabilistic changes of maize productivity in the North China Plain, our study demonstrated an advanced super-ensemble-based probabilistic projection approach in addressing the impacts of climate variability (change) on regional agricultural production and the uncertainties. (C) 2009 Elsevier B.V. All rights reserved.

We applied a remote-sensing method using MODIS time-series imagery to detect the expansion of intensive farming systems (shrimp farming and triple rice cropping) in the Soc Trang and Bac Lieu provinces of the Vietnamese Mekong Delta (VMD). Field surveys for interpreting the estimates derived from the remote-sensing analyses were conducted in June and September 2007. From 2000 to 2006, there has been rapid expansion of inland aquaculture, especially shrimp farming, in the western part of the Bac Lieu province and the coastal regions of both the Bac Lieu and Soc Trang provinces. The triple rice-cropped area expanded concentrically from specific small locations in the Bac Lieu province from 2001 to 2006. The triple rice-cropped areas in Soc Trang province also expanded concentrically from 2001 to 2003, but some of these new triple rice-cropped areas returned to double rice cropping after 2004. interviews with farmers and ancillary hydrological monitoring data suggested that changes in the water environment and transmission of information about new farming systems are closely connected with the annual expansion of triple rice-cropped areas in both provinces. The availability of salt-free irrigation water in the dry season may have led some pioneering farmers to attempt full-scale dry-season cropping. Furthermore, successful dry-season harvests and recent increases in the price of rice might have encouraged neighboring farmers to follow suit. Based on MODIS time-series imagery with field interviews at land-use change hotspots, the spatial structure of coastal farming systems and their recent changes were revealed from a macroscopic view point. (C) 2009 Elsevier B.V. All rights reserved.

This paper briefly introduces an integrated remote-sensing methodology based oil MODIS time-series imagery for detecting spatiotemporal changes in Vietnamese Mekong Delta (VIVID) farming systems. The integrated methodology consists of six parts, and uses a wavelet-based filter to smooth MODIS-derived time-series indexes (EVI, LSWI, and DVEL) from 2000 to 2008. Through a proposed decision tree using the smoothed indexes, we classified several farming systems, viz., triple rice cropping, two types of double rice cropping, shrimp-rice farming (rotational cropping), and inland aquaculture (monoculture). The MODIS-derived estimate of the total rice-planted area, shrimp-rice farming area, and inland aquaculture area agreed well with statistical data at the province level (R-2 &gt;= 0.96). However, in sonic provinces, the estimate has a large margin of error, probably because of the mixed-pixel effect due to the moderate spatial resolution of MODIS (250 m). According to the estimated spatial pattern of the farming systems in the whole VMD, inland aquaculture and shrimp-rice farming areas are distributed mostly in the coastal provinces. The areas of the farming systems steadily expanded until 2007, and double rice cropping systems in both Upper and coastal regions were replaced by triple rice cropping because of infrastructure improvements. The proportion of the triple rice cropping area peaked in 2005 and then declined steadily over the next 2 years. We discuss the advantage of the proposed methodology for detecting the spatiotemporal changes of land use patterns, especially of farming systems in a regional area.

Process-based crop models are increasingly being used to investigate the impacts of weather and climate variability (change) on crop growth and production, especially at a large scale. Crop models that account for the key impact mechanisms of climate variability and are accurate over a large area must be developed. Here, we present anew process-based general Model to capture the Crop-Weather relationship over a Large Area (MCWLA). The MCWLA is optimized and tested for spring maize on the Northeast China Plain and summer maize on the North China Plain, respectively. We apply the Bayesian probability inversion and a Markov chain Monte Carlo (MCMC) technique to the MCWLA to analyze uncertainties in parameter estimation and model prediction and to optimize the model. Ensemble hindcasts (by perturbing model parameters) and deterministic hindcasts (using the optimal parameters set) were carried out and compared with the detrended long-term yields series both at the crop model grid (0.5 degrees x 0.5 degrees) and province scale. Agreement between observed and modelled yield was variable, with correlation coefficients ranging from 0.03 to 0.88 (p &lt; 0.01) at the model grid scale and from 0.45 to 0.82 (p &lt; 0.01) at the province scale. Ensemble hindcasts captured significantly the interannual variability in crop yield at all the four investigated provinces from 1985 to 2002. MCWLA includes the process-based representation of the coupled CO2 and H2O exchanges; its simulations on crop response to elevated CO2 concentration agree well with the controlled-environment experiments, suggesting its validity also in future climate. We demonstrate that the MCWLA, together with the Bayesian probability inversion and a MCMC technique, is an effective tool to investigate the impacts of climate variability on crop productivity over a large area, as well as the uncertainties. (C) 2008 Elsevier B.V. All rights reserved.

Food security in China, the world's most populous country, has long been a concern because of the challenges of population growth, water shortages, and loss of cropland through urbanization, soil degradation, and climate change. Here, we present an integrated analysis of China's food demand and supply under IPCC Special Report on Emissions Scenarios A1, A2, B1, and B2 in 2020, 2050, and 2080, based on official statistics and future development scenarios. Our analysis accounts for future socioeconomic, technological, and resource developments, as well the impact of climate change. We present a covariant relationship between changes in cereal productivity due to climate change and the cereal harvest area required to satisfy China's food demand. We also estimated the effects of changing harvested areas on the productivity required to satisfy the food demand; of productivity changes due to climate change on the harvest area required to satisfy food demand; and of productivity and land use changes on the population at risk of undernutrition. China could be able to feed herself without disturbing the global food market in the twenty-first century, but whether the government will choose self-sufficiency or increased food imports may depend on the cost of change, which remains unknown.

This study attempts a new approach using Moderate Resolution Imaging Spectroradiometer (MODIS) time-series imagery to evaluate the agro-ecological interpretation of rice-cropping systems in flood-prone areas. A series of wavelet-based methodologies were applied to reveal the dynamic relationships among annual flood inundation, rice phenology, and land-use change in the Vietnamese Mekong Delta (VWD). The rice-heading dates of multi-cropping areas were estimated by detecting the local maximal points in smoothed Enhanced Vegetation Index (EVI) profiles, Using the Wavelet-based Filter for determining Crop Phenology (WFCP) and Wavelet-based Filter for evaluating the spatial distribution of Cropping Systems (WFCS) methods. The temporal information for annual flood intensity was determined for the six annual flood seasons over the period from 2000 to 2005 by the Wavelet-based Filter for detecting spatio-temporal changes in the Flood Inundation (WFFI) method. Analysis using remote sensing techniques revealed an interaction between the regional environment and agricultural activity in the VMD. First, comparing the estimated heading date of the winter-spring rice with the end date of flood inundation showed that the cropping season for the winter-spring rice in the flood-prone area fluctuates depending on the annual change in flood scale. This result implied that the onset Of winter-spring rice is spatially and temporally linked to the variable flood-recession season, and hence the annual change in flood scale. Secondly, the field survey study of the yearly change in the rice-cropping system in the An Giang province from 2000 to 2006 showed that the triple rice-cropped area in the An Giang province expanded from 2000 to 2005, because the construction of a ring-dyke system and water-resource infrastructure allowed an additional rice crop to be sustained during the flood season. However, the area of the third rice crop in the An Giang province decreased drastically in 2006 as a result of the management of pest outbreaks. Although the regional water-resource environment was gradually transformed by the construction of water-resource infrastructure, in order to achieve favorable conditions for a third rice crop during the flood season, the rapid change in land-use for agricultural activity may complicate the spatio-temporal configuration of the agricultural environment in the VMD. This case study used MODIS time-series imagery to help understand the functions of the macro-scale ecosystem, including annual flood regimes and human activity,.

A Bayesian approach, the Markov Chain Monte Carlo (MCMC) technique, was applied to a newly developed large-scale crop model for paddy rice to optimize a new set of regional-specific parameters and quantify the uncertainty of yield estimation associated with model parameters. The developed large-scale model is process-based and up-scaled from a conventional field-scale model to meet the intended spatial-scale of the large-scale model to the typical grid size of high-resolution climate models. The domain of the large-scale model covers all of Japan, but the crop simulation is conducted for each local governmental area in Japan. The MCMC technique exhibits powerful capability to optimize multiple parameters in a nonlinear and fairly complex model. The application of the Bayesian approach is useful to quantify the uncertainty of model parameters in a comprehensive manner when researchers on crop modeling analyze the uncertainty of yield estimation associated with model parameters under given observations. A sensitivity analysis of the large-scale model was conducted with the obtained posterior distribution of parameters and warming conditions that have never been experienced before to demonstrate the change in the uncertainty of yield estimation associated with the uncertainty of parameters of the large-scale model. The uncertainty of yield estimation under warming conditions was larger than that obtained under climate conditions that have been experienced before. This raises a concern that the uncertainty of impact assessment on crop yield may increase if future climate projections are fed to crop models with parameters optimized under current climate conditions. (C) 2008 Elsevier B.V. All rights reserved.

Soil CO2 concentrations to a depth of 50 cm depth were monitored with Vaisala CO2 probes from June 2004 to May 2005 in conventionally plowed and no-tillage agricultural plots and found to be generally higher in the former than the latter. Soil CO2 concentrations were also higher in summer and lower in winter, but were modulated by harvesting and the application of crop residues. Analysis of diurnal variations in soil CO2 concentrations, soil CO2 effluxes, and soil temperatures revealed distinct differences between plots. The relative amplitudes of the variation in CO2 concentrations peaked at a soil depth of 5 cm. Soil CO2 concentrations were higher when there was higher soil water content after precipitation, while soil CO2 effluxes were lower under the same circumstances. These two results relate to the lower diffusivity at higher soil water content, which restricts CO2 efflux, leading to higher soil CO2 concentrations. Calculations reveal that in the shallower soil layer (0-5 cm), CO2 production was greater in no-tillage plots, whereas in the deeper layer (15-25 cm), it was greater in conventional plots. © 2009, The Society of Agricultural Meteorology of Japan. All rights reserved.

The Intergovernmental Panel on Climate Change (IPCC) published the Fourth Assessment Report (AR4) in 2007 and stated that recent climate change and variation are induced by increases in the atmospheric greenhouse gases (GHG) concentration due to anthropogenic activities. The report includes the results of impact assessments on a wide range of sectors. These assessments have been conducted based on future climate projections, which refer to aspects of the future climate evaluated by Atmosphere-Ocean Coupled General Circulation Models (CGCMs). The projection data used in the AR4 are archived under the Program for Climate Model Diagnosis and Intercomparison (PCMDI) promoted by the U.S. Department of Energy. We interpolated the projection data around Japan and constructed a dataset entitled the "Mesh climate change data of Japan Ver. 2" for the climate change impact study. Nine projections performed by seven models under the A1B and A2 of the Special Report on Emissions Scenarios (SRES) were implemented for the dataset. They consist of mesh data with a size of 7.5' in longitude and 5.0' in latitude, i.e. approximately 10×10 km (45" in longitude and 30" in latitude, approximately 1×1 km, for one high-resolution model). The dataset includes five climatic elements, i.e. the daily mean, maximum, and minimum surface air temperatures, daily total precipitation, and daily accumulated shortwave radiation for three periods, 1981-2000, 2046-2065, and 2081-2100. This article describes the details concerning the construction and characteristics of the data. © 2009, The Society of Agricultural Meteorology of Japan. All rights reserved.

For this study, we developed and validated two types of impact functions on regional paddy rice yield in six regions of Japan. One is simple, and the other is slightly detailed. An impact function has the following features: (1) it is a look-up table that shows the quantity of interest (regional paddy rice yield in this study) in response to various forcing conditions
(2) it is provided by a mechanistic but heavy computational-load model
and (3) it is implemented in integrated impact assessment models to reduce the simulation time. The developed impact functions will be submitted for use in integrated impact assessment models and will contribute to examining the target level of stabilization on greenhouse gas emissions. The impact functions were compared with the observation and the conventional crop model simulation (full-simulation). The slightly detailed (simple) impact function agreed with the observation with a mean-absolute-percentage (MAP) error of 7.8% (7.7%). Under warming conditions, the slightly detailed (simple) one was comparable to the full-simulation with a MAP difference of 4.5% (4.6%). The majority of the difference arose from the temperature fluctuation within a short time period of less than two months. These results indicate that the application of the impact functions is permissible for assessing the impact of the average climate change on regional yield. However, the impact functions tend not to be a very reliable tool for assessing the impacts of extreme climate events and interannual climate variation on regional yield. © 2009, The Society of Agricultural Meteorology of Japan. All rights reserved.

A remarkable declining trend in rice quality has already been observed in western Japan and the future global warming associated with climate change is likely to exacerbate such risk. In this study, a simple statistical model was constructed to estimate the rice quality, which is defined as the proportion of white immature grains on a prefectural scale, in terms of two major climate variables during the grain-filling stage: the cumulative weighted effective temperature δ and the cumulative solar radiation SR. In order to account for the uncertainties included in the processes involved, Bayesian inference was used to estimate the model parameters. The modeled time changes in rice quality correlated well to those observed. Specifically, the reproducibility of rice quality since 2000 was particularly high. These results suggested that a combination of the two climate factors was responsible for the recent variability in rice quality on a prefectural scale. Subsequently, the elasticity of rice quality relative to δ and SR was examined based on the model. The elasticity represents the relative change in rice quality in response to a change in δ or SR, with a positive (negative) sign indicating increased (decreased) quality. Consequently, the mean elasticity of the rice quality relative to SR was larger than that to δ in Kyushu. Moreover, the time changes of rice quality in Kyushu synchronized with that of SR under high temperature conditions, suggesting that rice plants become more sensitive to conditions of insufficient radiation if exposed to high temperatures. Consequently, it was concluded that the contribution of the radiation condition to the variation in rice quality become relatively larger along with the recent increase in temperature. © 2009, The Society of Agricultural Meteorology of Japan. All rights reserved.

Understanding climate-yield relationships and the impacts of recent climate trends on crop productivity on a large scale is an important step in predicting regional agricultural production. In this study we investigated climate-crop relationships, recent trends in seasonal climate (maximum and minimum temperatures, diurnal temperature range and precipitation) and their impacts on the yields of major crops (i.e. rice, wheat, maize and soybean) at provincial scales throughout China over the last few decades. We found that major crop yields were significantly related to growing season climate in the main production regions of China, and that growing season temperature had a generally significant warming trend. Due to the trends in growing season climate, total rice production in China was estimated to have increased by 3.2 x 10(5) t decade(-1) during the period 1951-2002; total production of wheat, maize and soybean changed by -1.2 x 10(5), -21.2 x 10(5) and 0.7 x 10(5) t decade(-1), respectively, during 1979-2002. The warming trend increased rice yield in northeast China and soybean in north and northeast China; however, it decreased maize yield in 7 provinces (autonomous region or municipality) and wheat yield in 3 provinces. Our analysis presents the general response patterns of regional agricultural productivity to seasonal climate variability and change over the last few decades. Crop response mechanisms to local seasonal climate change (and variability) need further investigation to better understand the patterns and predict future consequences of climate change and variability on regional agricultural production.

This study investigates rice productivity in the coastal area of the Mekong River Delta, Vietnam focusing on relationships with the influences of seawater intrusion and recent changes in the land use at regional scale. We examined the statistical data on rice production at 30 districts of 4 provinces in the coastal area for the years 2003-2005, together with the average salinity concentration observed at 48 points in canals and rivers during the dry season. As an index of the extent of seawater intrusion to be compared with rice statistical data in each district, average salinity of each district was derived by using spatial interpolation from the data in the observation points. It was shown that seawater intrusion was the major factor leading to regional differences in rice cropping systems and land use patterns in the region. Rice cropping intensities, which is defined as the ratio of planted area to district area, decreased with increasing salinity level in canal water, but rice yields averaged over the district are independent of the salinity level. To avoid salinity stress to rice growth, rice cultivation in the coastal area is mainly undertaken during the period when seawater intrusion is weakened by the seasonal decrease in salinity in the rainy season. Specifically, in districts with high salinity, the salinity-free duration required for rice cultivation is short. There, rice cropping intensities are potentially limited by the salinity. In addition, recently, the area of paddy fields in the coastal area has been decreased through land use conversion to aquaculture, especially shrimp farms. Intensity of aquaculture, which requires brackish water, was also limited by seawater intrusion. Thus., rice production in the coastal area of the Mekong Delta was limited by the interrelationship between seawater intrusion and land-use diversification.

This study investigates rice productivity in the coastal area of the Mekong River Delta, Vietnam focusing on relationships with the influences of seawater intrusion and recent changes in the land use at regional scale. We examined the statistical data on rice production at 30 districts of 4 provinces in the coastal area for the years 2003-2005, together with the average salinity concentration observed at 48 points in canals and rivers during the dry season. As an index of the extent of seawater intrusion to be compared with rice statistical data in each district, average salinity of each district was derived by using spatial interpolation from the data in the observation points. It was shown that seawater intrusion was the major factor leading to regional differences in rice cropping systems and land use patterns in the region. Rice cropping intensities, which is defined as the ratio of planted area to district area, decreased with increasing salinity level in canal water, but rice yields averaged over the district are independent of the salinity level. To avoid salinity stress to rice growth, rice cultivation in the coastal area is mainly undertaken during the period when seawater intrusion is weakened by the seasonal decrease in salinity in the rainy season. Specifically, in districts with high salinity, the salinity-free duration required for rice cultivation is short. There, rice cropping intensities are potentially limited by the salinity. In addition, recently, the area of paddy fields in the coastal area has been decreased through land use conversion to aquaculture, especially shrimp farms. Intensity of aquaculture, which requires brackish water, was also limited by seawater intrusion. Thus., rice production in the coastal area of the Mekong Delta was limited by the interrelationship between seawater intrusion and land-use diversification.

The authors constructed the framework for a preliminary assessment of climate change impact on the rice insurance payout in Japan. The framework consisted of various models ranging from climate projection downscaling, rice yield estimation, yield loss assessment, and rice insurance payout estimation. In this study, a simulation was conducted based on the dynamically downscaled regional climate projection with a lateral boundary condition given by the global climate projection of the Meteorological Research Institute Coupled General Circulation Model, version 2 (MRI CGCM2), under the A2 scenario of the Special Report on Emission Scenarios (SRES). Results indicated that rice yield in the 2070s will decrease slightly in central and western Japan and increase in northern Japan. The increase in yield was derived from a significant reduction in yield loss caused by cool-summer damage; on the other hand, the decrease in yield was caused by the increase in yield loss caused by heat stress and the shortening of the growth period induced by the temperature rise. The increase in the atmospheric CO2 concentration resulted in an increase in paddy rice biomass because of the fertilization effect; however, the increase in biomass was not enhanced much as a result of shortening of the growth period if early planting was not considered as an adaptation practice. Reflecting such changes in yield, the rice insurance payout significantly decreased in northern Japan but only slightly increased in the areas of central and western Japan. In total, the 9-yr mean payout in Japan in the 2070s decreased to 120.2 billion yen; the value corresponded to 87% of the payout averaged over 9 yr in the 1990s (1991-99).

With meteorological data of Harbin City and Jiamusi City during 1951-2005 combined with the characteristics of rice production, the impacts of global warming on chilling damage, spring's drought, high temperature injury and diease and insect pests of rice were studied in this paper. Based on the research, a series of practical strateges to deal with global warming, were put forward to step up rice yield stably. In addition, the problems that exist in recent research were analyzed. The new idea for further studies on global warming and reducting urban heat island effect were proposed.

This experiment was conducted to predict the effect of global warming on a pig growth performance estimated from changes of the mean ambient temperature. The animal experiment was conducted in a room controlled with constant temperature and humidity. In this room, twenty one pigs (42.1±5.5kg) were individually housed in a cage. Four treatment groups were 23±1°C (adequate, 7 pigs), 28±1°C (moderate high temperature, 7 pigs), 30±1°C (moderate high temperature, 4 pigs), and 33±1°C (high temperature, 3 pigs). Relative humidity in all treatment groups was kept constant at 60±10%. The relation between ambient temperature and growth performance were analyzed and the following results were obtained. At 24.5°C, 27.3°C and 30.4°C, average daily gain decreased by 5%, 15% and 30%, respectively, compared to that at 23°C. Similarly, at 25.9°C, 30.3°C and 33.8°C, average daily feed intake decreased by 5%, 15% and 30%, respectively, compared to that at 23°C. The summer (June, July, August and September) data were analyzed with a program that illustrates the temperature range and the area on the map. The database of “Climate Change Mesh Data (Japan)” was used to calculate the annual mean temperature under the climate change scenario at 10×10 km mesh. With the progress of year from present to 2030's and 2060's, the area where the average daily gain reduced will expand to the north of Japan and its reducing degree will be accelerated. In August at the present, the decrease of average daily gain is already observed in west area. In 2060's August, however, its severe reduction will be developed in almost all area of Japan except Hokkaido. These results indicate that global warming will greatly affect the pig production in future Japan.

The combined impact of sea level rise and reduction of the Mekong River flow in the dry season on salinity intrusion and rice cropping in the Vietnamese Mekong Delta was assessed in this study. The MIKE11 model was used to simulate flow and salinity intrusion from December to June for the medium-term (mid-2030s) and long-term (mid-2090s) scenarios using data derived from the SRES B2 climate change projection. The sea level rise values for two scenarios were +20 cm and +45 cm, while the rates of change for the Mekong River flow were -15% and -29%, respectively. The results obtained for the mid-2030s and mid-2090s scenarios show that the 2.5 g/L saline front is likely to shift upstream by 10 km and 20 km in the main river channels, and up to 20 km and 35 km in the paddy field, respectively. The simulated salinity intrusion results were then used to compute durations of available water for irrigating rice cropping. The results indicate that area where triple rice crops are possible, will be reduced by approximately 71,000 and 72,000 ha, while single crop areas will be increased by approximately 38,000 and 179,000 ha for the mid-2030s and mid-2090s scenarios, respectively. Using GIS techniques the delta was divided into three areas reflecting different rice crop vulnerability levels
areas of high and medium vulnerability measured approximately 200,000 ha and 400,000 ha, respectively. © 2008, The Society of Agricultural Meteorology of Japan. All rights reserved.

For this study, we developed a new statistical model to estimate the daily accumulated global solar radiation on the earth's surface and used the model to generate a high-resolution climate change scenario of the radiation field in Japan. The statistical model mainly relies on precipitable water vapor calculated from air temperature and relative humidity on the surface to estimate seasonal changes in global solar radiation. On the other hand, to estimate daily radiation fluctuations, the model uses either a diurnal temperature range or relative humidity. The diurnal temperature range, calculated from the daily maximum and minimum temperatures, and relative humidity is a general output of most climate models, and pertinent observation data are comparatively easy to access. The statistical model performed well when estimating the monthly mean value, daily fluctuation statistics, and regional differences in the radiation field in Japan. To project the change in the radiation field for the years 2081 to 2100, we applied the statistical model to the climate change scenario of a high-resolution Regional Climate Model with a 20-km mesh size (RCM20) developed at the Meteorological Research Institute based on the Special Report for Emission Scenario (SRES)-A2. The projected change shows the following tendency: global solar radiation will increase in the warm season and decrease in the cool season in many areas of Japan, indicating that global warming may cause changes in the radiation field in Japan. The generated climate change scenario for the radiation field is linked to long-term and short-term changes in air temperature and relative humidity obtained from the RCM20 and, consequently, is expected to complement the RCM20 datasets for an impact assessment study in the agricultural sector. © 2008, The Society of Agricultural Meteorology of Japan. All rights reserved.

Land surface phenology dynamics reflect the response of the Earth&apos;s biosphere to inter- and intra-annual dynamics of the Earth&apos;s climate and hydrologic regimes. Investigations of land surface phenology dynamics and its relation to long-term climate variation could help us to detect the response of regional vegetation to climate variation. The present study developed a new algorithm for detecting regional land surface phenology dynamics (ARLSPD) and demonstrated it in detecting the vegetation response to inter-annual climate variability in the North East China Transect (NECT), a mid-latitude semi-arid terrestrial transect with strong gradients in environmental conditions and vegetation formations. The spatial-temporal patterns of greenup-onset date, maturity date, and senescence date during the period of 1982-2000 are presented. The resultant spatial-temporal patterns of land surface phenology were quite consistent with the land-cover characteristics, moisture, and temperature gradients. The relations between inter-annual variations in phenology and seasonal climate were investigated. It was found that besides human disturbance, land surface phenology depended primarily on the combined effects of preseason temperature and precipitation. The relative influence of preseason temperature and precipitation on land surface phenology was changing, which led to the different responses of land surface dynamics to climate variation along the moisture gradient in the NECT. In the arid and semi-arid region of NECT, the dates of onset for phonological events in temperate typical grassland were most significantly related to the precipitation during the preceding 2-4 months. Temperature-induced drought stress during the preceding months could delay greenup onset in cropland/grassland mosaic, and advance senescence in temporal typical grassland, and in cropland/grassland mosaic. The regional phenology algorithm, theoretically also applicable for complex ecosystems characterized by annual multiple growth cycles, is expected to couple with large-scale biogeochemical models to regulate dynamically land surface phenology.

Uncertainties in global climate models (GCMs) and emission scenarios affect assessments of the impact of global warming as well as the communication of scientific results. Here, we developed a probabilistic technique to deal with the uncertainties and to simulate the impact of global warming on rice production and water use in China, against a global mean temperature (GMT) increase scale relative to 1961-1990 values. From 20 climate scenarios output from the Intergovernmental Panel on Climate Change Data Distribution Centre, we used Monte Carlo analysis to develop the most likely climate-change scenarios for representative stations and derived the CERES-Rice model of [Alocilja, E.C., Ritchie, R.T., 1988. Rice simulation and its use in multicriteria optimization, IBSNAT Research Report Series 01] to simulate rice production under baseline and future climate scenarios. Adaptation options such as automatic application of irrigation and fertilization were considered, although cultivars were assumed constant over the baseline and future. After assessing representative stations across China, we projected changes in rice yield, growing period, evapotranspiration, and irrigation-water use for GMT changes of 1, 2, and 3 degrees C in a probabilistic way. Without consideration of CO2-fertilization effects, our results indicate that the growing period would shorten with 100% probability; yield would decrease with a probability of 90%, 100%, and 100% for GMT change of 1, 2, and 3 degrees C, respectively. The median values of yield decrease ranged from 6.1% to 18.6%,13.5% to 31.9%, and 23.6% to 40.2% for GMT changes of 1, 2, and 3 degrees C, respectively. According to the median values of the projected changes, evapotranspiration and irrigation water use would decrease in most of the investigated stations. If CO2-fertilization effects were included, the rice growing period would also be reduced with 100% probability; across the stations the median values of yield changes ranged from -10.1% to 3.3%, -16.1% to 2.5%, and -19.3% to 0.18% for GMT changes of 1, 2, and 3 degrees C, respectively. Evapotranspiration and irrigation water use would decrease more and with higher probability in comparison with the simulations without consideration of CO2-fertilization effects. Our study presents a process-based probabilistic assessment of rice production and water use at different GMT increases, which is important for identifying which climate-change level is dangerous for food security. (c) 2007 Elsevier B.V. All rights reserved.

This paper presents the methodology used to detect temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta (VMD) based on MODIS time-series imagery (Wavelet-based Filter for detecting spatio-temporal changes in Flood Inundation; WFFI). This methodology involves the use of a wavelet-based filter to interpolate missing information and reduce the noise component in the time-series data, as proposed in a previous study. The smoothed time profiles of Enhanced Vegetation Index (EVT), Land Surface Water Index (LSWI), and the Difference Value between EVI and LSWI (DVEL) are obtained from MOD09 8-day composite time-series data (resolution: 500 m; time period: 2000-2005). The proposed algorithm was applied to produce time-series inundation maps (WFFI products) for the five annual flood seasons over the period from 2000 to 2004. The WFFI products were validated via comparisons with Landsat-derived results and inundation maps based on RADARSAT images, hydrological data, and digital elevation model data. Compared with the RADARSAT-derived inundation maps at the province level, the obtained RMSE range from 364 to 443 km(2) and the determination coefficients [R-2] range from 0.89 to 0.92. Compared with Landsat-derived results at the 10-km grid level, the obtained RMSE range from 6.8 to 15.2 km(2) and the determination coefficients [R-2] range from 0.77 to 0.97. The inundated area of flooded forests/marsh to the northeast of Tonle Sap Lake were underestimated, probably because of extensive vegetation cover in this area. The spatial characteristics of the estimated start dates, end dates, and duration of inundation cycles were also determined for the period from 2000 to 2004. There are clear contrasts in the distribution of the estimated end dates and duration of inundation cycles between large-scale floods (2000-2002) and medium- and small-scale floods (2003 and 2004). At the regional scale, the estimated start dates for the southern part of An Giang Province during 2003 and 2004 was distinctly later than that for surrounding areas. The results indicate that these triple-cropping areas enclosed by dikes increased in extent from 2003 to 2004. In contrast, the estimated end dates of inundation at the Co Do and Song Hart State Farms were clearly earlier than those for surrounding areas, although the estimated start dates were similar. Temporal changes in the inundation area of Flood pixels in the Dong Thap and Long An Provinces are in excellent agreement with daily water-level data recorded at Tan Chan Station. The estimated area of Long-term water body increased in size from 2000 to 2004, especially in coastal areas of the Ca Mau and Bac Lieu Provinces. Statistical data for Vietnam indicate that this trend may reflect the expansion of shrimp-farming areas. The WFFI products enable an understanding of seasonal and annual changes in the water distribution and environment of the Cambodia and the VMD from a global viewpoint. (C) 2007 Elsevier Inc. All rights reserved.

Background: Changes in the timing of phenological events may cause the annual carbon budget of deciduous forests to change. Therefore, one should take such events into account when evaluating the effects of global warming on deciduous forests. In this article, we report on the results of numerical experiments done with a model that includes a phenological module simulating the timing of bud burst and other phenological events and estimating maximum leaf area index. Results: This study suggests that the negative effects of warming on tree productivity (net primary production) outweigh the positive effects of a prolonged growing season. An increase in air temperature by 3°C (5°C) reduces cumulative net primary production by 21.3% (34.2%). Similarly, cumulative net ecosystem production (the difference between cumulative net primary production and heterotrophic respiration) decreases by 43.5% (64.5%) when temperatures are increased by 3°C (5°C). However, the positive effects of CO2 enrichment (2 × CO2) outweigh the negative effects of warming (&lt
5°C). Conclusion: Although the model was calibrated and validated for a specific forest ecosystem, the implications of the study may be extrapolated to deciduous forests in cool-temperate zones. These forests share common features, and it can be conjectured that carbon stocks would increase in such forests in the face of doubled CO2 and increased temperatures as long as the increase in temperature does not exceed 5°C. © 2007 Toda et al
licensee BioMed Central Ltd.

This paper assesses the impact of climate change on irrigated rice yield using B2 climate change scenario from the Regional Climate Model (RCM) and CERES-rice model during 2071--2090. Eight typical rice stations ranging in latitude, longitude, and elevation that are located in the main rice ecological zones of China are selected for impact assessment. First, Crop Estimation through Resource and Environment Synthesis (CERES)-rice model is validated using farm experiment data in selected stations. The simulated results represent satisfactorily the trend of flowering duration and yields. The deviation of simulation within +/- 10% of observed flowering duration and +/- 15% of observed yield. Second, the errors of the outputs of RCM due to the difference of topography between station point and grid point is corrected. The corrected output of the RCM used for simulating rice flowering duration and yield is more reliable than the not corrected. Without CO2 direct effect on crop, the results from the assessment explore that B2 climate change scenario would have a negative impact on rice yield at most rice stations and have little impacts at Fuzhou and Kunming. To find the change of inter-annual rice yield, a preliminary assessment is made based on comparative cumulative probability at low and high yield and the coefficient variable of yield between the B2 scenario and baseline. Without the CO2 direct effect on rice yield, the result indicates that frequency for low yield would increase and it reverses for high yield, and the variance for rice yield would increase. It is concluded that high frequency at low yield and high variances of rice yield could pose a threat to rice yield at most selected stations in the main rice areas of China. With the CO2 direct effect on rice yield, rice yield increase in all selected stations.

To investigate the possibility of projection of unusual weathers such as hot and cool summer or disaster of heavy rainfall under the global warming condition, we have examined the reconstructed probability of extreme summertime high and low temperatures and local-scale heavy rainfall by using a regional climate model (RCM), Regional Atmospheric Modeling System (RAMS) modified in the Terrestrial Environmental Research Center (TERC). The TERC-RAMS is a nested RCM and has driven by a forcing general circulation field obtained from realistic re-analysis data or Global Climate Models. Preliminary experiments are intended to reconstruct mainly on daily rainfall amount and frequency, and daily maximum and minimum temperatures in Japan. The results showed that the TERC-RAMS could reconstruct the rainfall frequency in one month, but not project the absolute amount of heavy rainfall well. In addition, this model has a systematic bias for under-estimation of temperature. The model experiments have necessity to further development especially on radiation and surface soil/water conditions.

A warming trend has become pronounced since the 1980s in China and is projected to accelerate in the future. Concerns about the vulnerability of agricultural production to climate change are increasing. The impact of future climate change on crop production has been widely predicted by using crop models and climate change scenarios, but little evidence of the observed impacts of climate change on crop production has been reported. In this study, we synthesized crop and climate data from representative stations across China during 1981-2000 to investigate whether there were significant trends in changes of climate variables in different regions, and whether theses changes have had significant impact on the development and production of the staple crops (i.e. rice, wheat, and maize). Our results showed that significant warming trends were observed at most of the investigated stations, and the changes in temperature have shifted crop phenology and affected crop yields during the two decades. The observed climate change patterns, as well their impacts on crop phenology and yields are spatially diverse across China. Our study also highlights the need for further investigations of the combined impacts of temperature and CO2 concentration on physiological processes and mechanisms governing crop growth and production. (c) 2006 Elsevier B.V. All rights reserved.

A warming trend has become pronounced since the 1980s in China and is projected to accelerate in the future. Concerns about the vulnerability of agricultural production to climate change are increasing. The impact of future climate change on crop production has been widely predicted by using crop models and climate change scenarios, but little evidence of the observed impacts of climate change on crop production has been reported. In this study, we synthesized crop and climate data from representative stations across China during 1981-2000 to investigate whether there were significant trends in changes of climate variables in different regions, and whether theses changes have had significant impact on the development and production of the staple crops (i.e. rice, wheat, and maize). Our results showed that significant warming trends were observed at most of the investigated stations, and the changes in temperature have shifted crop phenology and affected crop yields during the two decades. The observed climate change patterns, as well their impacts on crop phenology and yields are spatially diverse across China. Our study also highlights the need for further investigations of the combined impacts of temperature and CO2 concentration on physiological processes and mechanisms governing crop growth and production. (c) 2006 Elsevier B.V. All rights reserved.

This study was carried out to assess the impact of global warming on the broiler meat production in Japan. The relation between ambient temperature and meat production was investigated and it was found that at 27.2 and 30.0°C, meat production decreased by 5 and 15%, respectively, compared to meat production at 23.0°C. The summer (July, August and September) data were analyzed with a program that illustrates the temperature range and the area on the map. The database of &quot;Climate Change Mesh Data (Japan)&quot; was used to calculate the annual mean temperature under the climate change scenario at all 10×10km mesh. In 2060&#039;s, the mean temperature of July, August and September in south Kyushu area (Miyazaki and Kagoshima city) and north Tohoku area (Aomori and Morioka city) was predicted to increase from 1.8-2.5°C and from 3.0-4.5°C, respectively. In all months, meat production will decrease gradually and with the progress of year, and serious effects were forecasted in Kyushu, Shikoku, Chugoku and Kinki area. In Tohoku area, where it is not a hot area, meat production will be affected with global warming. This area is main producer of Japanese chicken, therefore, effective measures to present the decrease in production are necesssary. In conclusion, global warming will greatly affect the chicken meat production in Japan by the middle of 21&lt;SUP&gt;st&lt;/SUP&gt; century.

We developed an automatic opening and closing chamber system (AOCC) based on an open-flow dynamic method (open-flow AOCC). The AOCC can be used during all four seasons, even at the surface of relatively deep snow. We compared the open-flow AOCC with two closed dynamic methods [the AOCC configured as a closed dynamic system (closed dynamic AOCC) and the LI-6400 system] under field conditions. The closed dynamic-AOCC and LI-6400 measurements were about 15.4% and 5.2% lower, respectively, than the values obtained with the open-flow AOCC. There was a significant difference in soil respiration rate between the open-flow AOCC and the closed dynamic AOCC system. In contrast, no significant difference in soil respiration rate was detected between the open-flow AOCC and the LI-6400 system. In the field, the open-flow AOCC permitted continuous long-term measurements under a range of temperature conditions and did a good job of reflecting the marked daily and seasonal variations in soil respiration as a function of soil temperature.

Using various plant materials, we identified two conceptual pools of plant litter, decomposable plant material (DPM) and resistant plant material (RPM), in the Rothamsted Carbon Model (RothC) by comparing the default proportions of DPM and RPM in the RothC and proportions in plant material fractions as determined by two-step acid hydrolysis with H2SO4. We collected 37 plant samples from 15 species at six sites on arable land, grassland, or forest in Japan. Carbon in the plant materials was divided into three pools by acid hydrolysis: (a) Labile Pool I (LP I), obtained by hydrolysis with 5 N H2SO4 at 105 degrees C for 30 min; (b) Labile Pool II (LP II), obtained by hydrolysis with 26 N H-2 SO4 at room temperature overnight, and then with 2 N H2SO4 at 105 C for 3 h; and (c) Recalcitrant Pool (RP), the unhydrolyzed residue. The average proportion of LP I in crops and grasses was 59%, which was the same as the proportion of DPM defined in the RothC as the default value for crops and grasses. The remaining 41% (23% LP II + 18% RP) was consequently the same as the RPM proportion defined in the RothC. Similarly, the average proportion of LP I in all tree leaves (19%) was very close to the proportion of DPM in the RothC (20%) for trees. These results indicate that DPM in the RothC can be identified as LP I from the acid hydrolysis analysis and RPM as LP II+RP. We conclude that, at least theoretically, the use of an independent DPM:RPM ratio, as determined by acid hydrolysis analysis for each plant material, should enable more reliable modeling of SOM dynamics than the use of default DPM:RPM values provided by the model, even though the practical advantages of this method require further evaluation. (c) 2005 Elsevier Ltd. All rights reserved.

We can precisely predict the future dynamics of populations only if we know the underlying mechanism of population dynamics. Long-term data are important for the elucidation of such mechanisms. In this article we analyze the 50-year dynamics of annual light-trap catches of three insect pest species living in paddy fields in Japan: the rice stem borer, Chilo suppressalis (Walker) (Lepidoptera: Pyralidae); the green rice leafhopper, Nephotettix cincticeps (Uhler) (Hemiptera: Deltocephalidae); and the small brown planthopper, Laodelphax striatellus (Fallen) (Hemiptera: Delphacidae). We separate the long-term dynamics into two components by using locally weighted scatterplot smoothing: (1) the underlying dynamics of populations, and (2) the influence of the past changes in the environment. The former component is analyzed by response surface analysis and vector autoregression to evaluate the nonlinearity of density-dependence and the inter-specific influence of density, respectively. On the basis of these analyses, we perform the state-space model analyses. The state-space model selected by Akaike's information criterion indicates that the observed number of light-trap catches of C. suppressalis and N. cincticeps in summer increases with increasing temperatures in the previous winter. It also indicates that the influence of temperature is not carried over to the next year. We utilize the selected model to predict the impact of global warming on these species, by substituting the temperature predicted by a general circulation model.

This study updates the projection of global warming impact on rice production in Japan using five coupled Atmosphere-Ocean General Circulation Model (AOGCM) products under the SRES A1B scenario. Projection of daily maximum and minimum temperatures and daily total solar radiation for each AOGCM product are fed to a regional-scale rice model by which the rice heading day and yield are simulated. Most climate model results show that while the heading day becomes significantly earlier, the yield mostly remains in the range of inter-annual variability of the present climate. Projected future yield shows a distinct pattern of increase in the northern Japan and decrease in the southwestern Japan, with exception in parts of Kyushu. Projected yield change in the central Japan differs depending on the location of the prefecture. The trend of change in yield is consistent among simulation years in the northern and central Japan whereas it varies year-to-year in the southwestern Japan. The variance in yield over southwestern Japan possibly becomes larger due to heat stress as a result of global warming.

Multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to estimate the spatial distribution of heading date and rice-cropping system employed in the Mekong Delta relative to seasonal changes in water resources in 2002 and 2003. We improved a Wavelet-based Filter for determining Crop Phenology (WFCP) and developed a Wavelet-based Filter for evaluating the spatial distribution of Cropping Systems (WFCS) to the interpretation of MODIS time-series data to determine the spatial distribution of rice phenology and various rice-cropping systems from the seasonal Enhanced Vegetation Index (EVI) data. The findings correspond well the physical characteristics of the cropping system in the Mekong Delta, which have changed over time in response to localized and seasonal changes in water resources. One such example is the double-irrigated rice-cropping system commonly employed in the upper Mekong Delta in the dry season to avoid damage due to the subsequent floods. The shortage of suitable irrigation water and intrusion of saline water in the coastal regions during the dry season has constrained the practice dry-season cropping and has meant that the double- and single-rainfed rice-cropping systems are employed in the rainy season. A triple-irrigated rice-cropping system is used in the central part of the Mekong Delta which is located midway between the flood-prone and salinity intrusion areas. Analysis of annual changes in the rice cropping systems between 2002 and 2003 showed that the triple-cropped rice expanded to the flood- and salinity-intrusion areas. This expansion indicates that the implementation of measures to limit the extent of flooding and salinity intrusion by improved farming technologies and improvements in land management. The heading dates in the upper Mekong Delta in 2003 were earlier than in 2002 by approximately 20 to 30 days. The reasons for this would be due to decreased flood runoff in 2002 compared to 2001, and implementation of government policies regarding early sowing of dry-season crops. Subsequent analysis of the MODIS data confirmed that the spatial distribution of rice-cropping systems was closely related to seasonal changes in river runoff regime in the Mekong Delta. (C) 2005 Elsevier Inc. All rights reserved.

We applied the Rothamsted Carbon Model (RothC), which was developed for simulating the soil organic carbon (SOC) turnover in non-waterlogged soils, for long-term experiments (16-22 years) on Japanese paddy soils and modified it to accurately simulate the changes in the content of SOC with time in paddy soils. The RothC underestimated the SOC content in all the nine plots at five sites: Gley Soils in Akita and Shimane, Gray Lowland Soils in Toyama and Mie, and Yellow Soil in Oita prefecture. This may be mainly due to the slow decomposition rate of organic matter during the rice-growing season, when submerged soils are waterlogged and subjected to anaerobic conditions. On the other hand, the decomposition of organic matter might be inhibited, not only during the submergence period but also throughout the year in paddy soils because of the difference in the composition of microorganisms between upland and paddy soils. Taking these possibilities of differences in the decomposition rate between upland soils and paddy soils into account, we changed the decomposition rates of the RothC during the submergence period (summer) and the period without submergence (winter), separately. We ran the model many times by changing the decomposition rates for summer and winter separately and tried to identify the optimum combinations of the values of the factors required to change the default decomposition rate, so that the modeled SOC content would be consistent with the values observed in nine plots at five experimental sites. To determine the optimum combinations of the values of the factors, we used two statistical indices, the root mean square error (RMSE), which represents the degree of coincidence, and the mean difference (M), which is a measure of model bias. We found that the optimum combination of the values of the factors required to change the decomposition rate was 0.2 in summer and 0.6 in winter. The modified RothC for paddy soils by simple tuning of the decomposition rate using the values of the factors (0.2 and 0.6) resulted in a much better performance than that of the original RothC for simulating the changes in the SOC content with time in Japanese paddy soils under various climatic conditions, types of soil texture, and management systems. This modified model can be used for the estimation of carbon loss from soils as well as for the planning of suitable organic matter management, at least in Japanese paddy soils.

Information of crop phenology is essential for evaluating crop productivity and crop management. Therefore we developed a new method for remotely determining phenological stages of paddy rice. The method consists of three procedures: (i) prescription of multi-temporal MODIS/Terra data; (ii) filtering time-series Enhanced Vegetation Index (EVI) data by time-frequency analysis; and (iii) specifying the phenological stages by detecting the maximum point, minimal point and inflection point from the smoothed EVI time profile. Applying this method to MODIS data, we determined the planting date, heading date, harvesting date, and growing period in 2002. And we validated the performance of the method against statistical data in 30 paddy fields. As for the filtering, we adopted wavelet and Fourier transforms. Three types of mother wavelet (Daubechies, Symlet and Coiflet) were used in Wavelet transform. As the results of validation, the wavelet transform performed better than the Fourier transform. Specifically, the case using Coiflet (order=4) gave remarkably good results in determining phenological stages and growing periods. The root mean square errors of the estimated phenological dates against the statistical data were: 12.1 days for planting date, 9.0 days for heading date, 10.6 days for harvesting date, and 11.0 days for growing period. The method using wavelet transform with Coiflet (order=4) allows the determination of regional characteristics of rice phenology. We proposed this new method using the wavelet transform; Wavelet based Filter for determining Crop Phenology (WFCP). (c) 2005 Elsevier Inc. All rights reserved.

Regional estimates or prediction of crop production is critical for many applications such as agricultural lands management, food security warning system, food trade policy and carbon cycle research. Remote sensing offers great potential for regional production monitoring and estimates, yet uncertainties associated with are rarely addressed. Moreover, although crops are one of critical biomes in global carbon cycle research, few evidences are available on the performance of global models of terrestrial net primary productivity (NPP) in estimating regional crop NPP. In this study, we use high quality weather and crop data to calibrate model parameter. validate and compare two kinds of remote sensing based production efficiency models, i.e. the Carnegie-Ames-Stan ford-Approach (CASA) and Global Production Efficiency Model Version 2.0 (GLO-PEM2), in estimating maize production across China. Results show that both models intend to underestimate maize yields, although they also overestimate maize yields much at some regions. There are no significant differences between the results from CASA and GLO-PEM2 models in terms of both estimated production and spatial pattern. CASA model simulates better in the areas with dense crop and weather data for calibration. Otherwise GLO-PEM2 model does better. Whether the water soil-moisture down-regulator is used or not should depend on the percent of irrigation lands at the regions. The improved and validated models can be used for many applications. Further improvement can be expected by increasing remote sensing image resolution and the number of surface data stations. (c) 2004 Elsevier B.V. All rights reserved.

We tested the Rothamsted Carbon Model (RothC) against three long-term (27-28 years) experimental sites on Thai upland soils in order to see how this widely used 'temperate' soil carbon turnover model performed in a typical farming region in the tropics. We were able to verify - over a much longer period than had been examined in previous studies - that RothC performs well in a tropical region in plots used for continuous cropping experiments of maize and cassava without organic matter application. However, the model overestimated soil organic carbon (SOC) in some plots to which large amounts of organic matter (rice straw or cassava stalks) were applied. This overestimate could not be attributed to errors in estimating either the amount of C input to the soil or the ratio of decomposable plant materials to resistant plant materials entering the soil. Among many factors affecting SOC dynamics (e.g. weather conditions, soil characteristics, etc.), which are different in tropical regions from temperate regions, we conclude that the activity of soil fauna might be a major factor which makes the performance of RothC worse where much organic matter was applied. We suggest that care should be taken when applying RothC to tropical soils with large amounts of added organic matter.

We examined the spatial pattern of rice phenology in the Red River Delta (RRD) by using time-series MODIS data in 2003. We applied the Wavelet based Filter for determining Crop Phenology (WFCP) to assess the seasonal change of EVI data reflecting from the crop status. Time courses of smoothed EVI derived by WFCP showed two peaks on April-March and August-September, indicating that double rice cropping is mainly being performed in RRD. It was also shown that there are winter-spring rice cropping areas in eastern RRD (Hai Duong province, Hai Phong province and Thai Binh province) where cultivation period is about two weeks earlier than the other provinces. Furthermore, spatially heterogeneous distribution of heading dates for rainy season rice was detected by the spatio-temporal EVI. MODIS EVI with WFCP enable us to grasp the geographical characteristics of agricultural activity in RRD i.e. spatial pattern of rice cropping system, phenology and its recent changing trend with high accuracy.

Water is one of the most critical resources in China. Climate change and soil degradation will be two major, interrelated environmental challenges faced by managers of water resources in coming decades. In this study, we used a water-balance model and updated databases to assess the interacting impacts of climate change and soil degradation on China's future water resources. We plotted the spatial pattern of changes in actual and potential evapotranspiration, soil moisture deficits, and surface runoff across China in the 2020s using a resolution of 0.5degrees latitude and longitude under scenarios based on climate change, soil degradation, and a combination of the two. The results showed that climate change would affect the magnitude and spatial pattern of water resources on a national scale. Some regions in central, southwestern, and northeastern China would become more vulnerable to disastrous drought and floods as a result of soil degradation. Under the combined impacts of climate change and soil degradation, soil moisture deficits would increase most in central, western, and southwestern China; surface runoff would increase most in southeastern China. More detailed process-based models are needed to capture feedback mechanisms more effectively.

The East Asian monsoon (EAM) and the El Nino Southern Oscillation (ENSO) determine climate variability over much of East Asia, affecting vulnerable grain markets and food security in China. In this study, we investigated the variability of climate and of agricultural production in China in association with the East Asian summer monsoon (EASM) and ENSO. Data from China showed that a strong EASM decreased fall temperature in Gansu and Sichuan Provinces in western China, as well as winter temperature in Heilongjiang Province in NE China and in Shandong and Anhui Provinces in eastern China. Summer rainfall in Hunan Province in southern China increased in weak EASM years. Summer temperature increased in Heilongjiang in NE China and Gansu Province in NW China during the La Nina phase. Summer rainfall decreased in Gansu Province in NW China during the El Nino phase. Among staple crops in China (rice, wheat, maize), maize production was very vulnerable to a strong EASM and El Nino phase. In Henan Province in central China, seasonal climate variability associated with EASM and ENSO resulted in about 14.4 and 15.6 %, respectively, of maize yield variability. Maize Yield at the national scale decreased significantly by 5.2 % during the El Nino phase. Cropland area affected and damaged by floods in Hunan Province in southern China increased significantly by 11.3 and 8.5 %, respectively, in weak monsoon years. During the La Nina phase, total crop planting area increased significantly in Shandong, Henan and Anhui Provinces in central China, and in Heilongjiang Province in NE China; however, it decreased significantly in Sichuan Province in SW China. The large variability in seasonal climate and agricultural production in association with EASM and ENSO warrant applying EASM and ENSO information to agricultural and food market management.

This paper describes a Multilayered Integrated Numerical Model of Surface Physics - Growing Plants Interaction (MINoSGI), which represents interactions between the dynamics of forest ecosystems and microclimate. Aiming at a large-scale study in the future, we describe forest dynamics by using area-averaged prognostic equations for thedistributions of plant density and plant weight with respect to plant height classes, instead of individual-based treatments for small-scale forest patches. Growth and mortality of plants are modelled based on the carbon balance of each plant height class. The area-averaged microclimate (e.g., light, wind speed, temperature, humidity, CO2 concentration) within the forest canopy is simulated by a one-dimensional multilayer canopy model, which includes most of the physical and physiological processes that control the forest microclimate. Owing to its multilayered framework, a direct specification is possible for the difference in the growing environment among plants of different size and species. Given hourly meteorological conditions, the model outputs energy, water, CO2 and momentum fluxes to and from a forest, of which the structure changes through competition among plants. The model's performance was tested by comparing its outputs with observed data on the development of plant size distribution taken over a 5-year period in an evergreen coniferous (Cryptomeria japonica) forest. The model produced realistic estimates of the total biomass increments during the period. The ratio of net primary production to gross primary production (=0.45) was consistent with previous estimates for temperate forests. The bimodal seasonal pattern in net ecosystem production was similar to the seasonal trend in the CO2 flux measured over a forest of the same species. Although some limitations due to the one-dimensional representation of microclimate were noticeable, the model adequately simulated distributions of annual growth rate, plant weight and diameter across plant height classes. Since the basic equations can be extended to include the effect of spatial variability with marginal increase of computational costs, the present model framework is feasible for large-scale studies.

This study was undertaken to assess the impact of global warming on the production in Japan of apple (Malus domestica Borkh.) and satsuma mandarin (Citrus unshiu Marc.). The annual mean temperature was used to simulate possible changes in favorable regions for the cultivation of apple and satsuma mandarin. The temperature ranges assumed to be appropriate for fruit production were 6-14 degreesC and 7-13 degreesC for apples and 15-18 degreesC for satsuma mandarins, respectively. The database of "Climate Change Mesh Data (Japan)" was used to calculate annual mean temperatures under the climate change scenario, derived from four Atmosphere-Ocean General Circulation Models (AOGCM) at all 10 X 10 km mesh. It was predicted that the favorable regions to cultivate apples and satsuma mandarins will gradually move northward. In 2060's the plains of central Tohoku will be unfavorable for apple cultivation, while most of Hokkaido will be suitable. The plains of northern Tohoku are predicted to attain the annual mean temperatures higher than those of the current main producing districts. By 2060's, the favorable areas for satsuma mandarin production will possibly move from the southern coastal sites to inland areas of western and southern Japan, the plains of Kanto and the littoral zones of the Japan Sea in the central and western Japan and in southern Tohoku. Therefore, global warming will greatly affect the cultivation environment of apples and satsuma mandarins in Japan by the middle of the 21st century.

It has become obvious in recent years that water is the most critical resource for Chinese agricultural ecosystems. Changes in agricultural water demands and soil moisture have significant implications for China's water supply, the potential for drought and flood, and agricultural production. In the studies, we explored the changing trends in agricultural water demands, the changing trends and variability in soil moisture associated with both drought and increased surface runoff in Chinese croplands during the last half-century, and their impacts on agricultural production. We plotted temporal and spatial changes in agricultural water demands, soil moisture, soil-moisture variability, soil-moisture deficit, yield index, and surface runoff on a grid of 0.5degrees resolution. We found a trend toward agricultural water demands increasing, soil drying and significant changes in soil-moisture variability on the North China Plain and the Northeast China Plain. There was a significant decrease in agricultural water demands and a significant increase in soil-moisture levels in Southwest China, and a generally insignificant increase or decrease trend in agricultural water demands and soil-moisture levels in Southeast China. These changes in agricultural water demands and soil-moisture levels had corresponding impacts on soil-moisture deficit, and consequently on agricultural production. Increased surface runoff was found in the mountainous areas of the southwest and northeast, and in some areas along the South Coast. (C) 2003 Elsevier Science B.V. All rights reserved.

Daily rainfall data for 187 stations in Sri Lanka spanning the period 1960-1996 were analyzed to investigate the spatial and temporal characteristics of the mean rainfall intensity (MRI) through this time interval with special focus on the Southwest Monsoon (May-September). Particular emphasis was laid on temporal changes in the MRI series. The mean and standard deviation (SD) of the MRI data showed considerable spatial variation. Regression analysis expressing precipitation as a function of time at the various stations revealed distinct spatial trends; the results point to hi-h MRI in lowland areas and low MRI in mountain areas. Principal Components Analysis of the temporal C relationships among a reduced set of stations located in an equal-sized grid showed that the three dominant principal components (PCs) are characterized by the maximum and minimum mean and SD of the MRI series together with the mean number of rainy days. The first, second and third PC modes show significant patterns of the MRI data series over the northern half. southern half and southwestern coastal belt of Sri Lanka, respectively. The time series pattern of the dominant PC modes revealed distinct changes in MRI over time. A noticeable higher value in MRI was found from 1977 to 1996, this tendency is most pronounced for the first PC mode. The time series of the Southern Oscillation Index was found to be closely related to changes in the MRI patterns associated with the first PC mode. In addition, El Nino years coincide with low values of the first PC mode. Some La Nina years show a positive response for the first and third PC modes, while there is no clear response for the MRI pattern identified by the second PC. (C) 2003 Elsevier Science B.V. All rights reserved.

The terrestrial water cycle and the impact of climate change are critical for agricultural and natural ecosystems. In this paper, we assess both by running a macro-scale water balance model under a baseline condition and 2 General Circulation Model (GCM)-based climate change scenarios. The results show that in 2021-2030, water demand will increase worldwide due to climate change. Water shortage is expected to worsen in western Asia, the Arabian Peninsula, northern and southern Africa, northeastern Australia, southwestern North America, and central South America. A significant increase in surface runoff is expected in southern Asia and a significant decrease is expected in northern South America. These changes will have implications for regional environment and socioeconomics.

Climate change would have a major impact on the hydrological cycle and consequently on available water resources, the potential for flood and drought, and agricultural productivity. In this study, the impacts of climate change on the agricultural water cycle and their implications for agricultural production in the 2020s were assessed by water-balance calculations for Chinese croplands. Temporal and spatial changes in potential evapotranspiration, actual evapotranspiration, soil-moisture, soil-moisture deficit, yield index, and cropland surface runoff under the baseline climate and a HADCM2 general circulation model (GCM) climate-change scenario were mapped on a grid of 0.5degrees latitude/longitude resolution. According to the analysis, agricultural water demand in south China is projected to decrease generally, and the cropland soil-moisture deficit would decrease due to climate change. However, in north China, agricultural water demand is expected to increase, and the soil-moisture deficit would increase generally. The changes in the water resources would have consequent impacts on the yield index. Cropland surface runoff during the growing period is expected to increase on some sloping croplands in the southwest mountain areas and in some areas along the south coast. These changes would have important implications for agricultural production. Particularly the rain-fed crops in the north China plain and northeast China would face water-related challenges in coming decades due to the expected increases in water demands and soil-moisture deficit,and decreases in precipitation. (C) 2002 Elsevier Science B.V. All rights reserved.

General circulation models (GCMs) that can simulate global climate are used to predict climate changes caused by an increase in atmospheric cO2 concentration. However, the spatial resolutions of currently running models are rough with a resolution of about 3° to 6° in latitude/longitude. Thus, reducing the relatively large-scale climatic states that the GCM provides to smaller-scale ones is required to evaluate impacts of climate changes on agriculture and natural ecosystems at local and regional scales. We constructed a dataset, namely the mesh climate change data of Japan, using the inverse distance weighted interpolation against coupled atmosphere-ocean GCMs' (A-O GCMs) experiment results under gradually increasing atmospheric cO2 concentration. The A-O GCMs used in this article are ECHAM4/OPYC3 (Germany), CGCM1 (Canada), CSIRO-Mk2 (Australia), and CCSR/NIES (Japan). The dataset gives the average climate change scenarios in Japan for every 10-year period over the next 100 years with a resolution of 7.5' in longitude and 5' in latitude (approximately 10 by 10 km). This article describes the construction method and contents of the dataset. In order to demonstrate the characteristics of the dataset, we examined the transient changes in spatial distribution of accumulated surface air temperature, accumulated precipitation, and mean short wave radiation during the months of May through September, corresponding with the major crop cultivation period in Japan. © 2003, The Society of Agricultural Meteorology of Japan. All rights reserved.

A study of the seasonal structure of rainfall organization in a humid tropical mountain region of West Africa using multivariate classification techniques revealed considerable variations in rainfall regimes from ultra-humid to sub-humid conditions. Principal Component Analysis produced a three-component model which was used to characterize and explain the seasonal rainfall variations within the region. These are the Dry Season component which accounts for most of the variance in the rainfall data and represents the December-April period
the Wet Season component (June-September) and the Wet/Dry and Dry/Wet transitions component in October-November and May, respectively. Using the three components and a hierarchical clustering procedure, seven clusters representing the rainfall regimes were identified and characterized. A better understanding of the regional rainfall climate is important to West African countries whose economies depend largely on agriculture and related activities governed by the reliability of rainfall. © 2002, The Society of Agricultural Meteorology of Japan. All rights reserved.

Global warming may affect crop damage caused by insect pest, by changing the degree of synchronization between pest occurrence and the susceptible stage of crops. The epidemiological system of rice stripe virus disease (RSV disease) transmitted by the small brown planthopper, Laodelphax striatellus (Fallen), is greatly influenced by synchronization, because the susceptible stage for virus infection is within several weeks after transplanting. We calculated how the area potentially vulnerable to RSV disease will change under future global warming by using the results of the Global Climate Model (GCM) experiments reported by the Intergovernmental Panel on Climate Change. For simplicity, assuming that rice seedlings are transplanted from May to June, we made a map, in which the number of generations of the small brown planthopper on June 1 was plotted by calculating the effective cumulative temperature. The influence of solar radiation was also considered in this calculation. We judged that the area located near the boundary of generations is potentially vulnerable to disease prevalence, because planthoppers are in the adult stage there. Generation maps indicated that the Tohoku and Hokuriku districts, which are major districts of rice production in Japan, might be potentially vulnerable to disease infection under future global warming.

We measured diurnal changes of CO2 flux from bare soil in agricultural field in central Japan. Measurements of soil CO2 flux were made by the open-flow IRGA method. Temperature and ambient CO2 concentration, which are highly variable in time, were also monitored during the measurements. The Soil CO2 flux showed significant diurnal changes, and these patterns were highly correlated with the soil surface temperatures. The measured values ranged from 2.52 x 10(-2) to 8.54 x 10(-2) Mg CO2 m(-2) s(-1) in August and from -4.95 x 10(-3) to 1.13 x 10(-2) mg CO2 m(-2) s(-1) in February. Moreover, a negative linear relationship between soil CO2 flux and ambient CO2 concentration was found in diurnal fluctuations. To explore the mechanism of these daily cycles, we estimated Soil CO2 flux with a coupled model of soil microbial activity and gas diffusion. The parameter functions used in our calculation were obtained based on the measured values during the same time of soil respiration measurements. The soil CO2 production rates were obtained by laboratory experiments using the undisturbed soil core samples, which were taken from the field measuring soil respiration. The calculated soil CO2 flux ranged from 4.41 x 10(-2) to 7.78 x 10(-2) Mg CO2 m(-2) s(-1) in August and 1.14 x 10(-3) to 5.03 x 10(-3) mg CO2 m(-2) s(-1) in February. These patterns of diurnal variability were similar for the measured ones, and the daily amounts estimated by integrating the model outputs were almost equal to those from the measured values. However, the amplitude of the measured respiration rates was larger than that of the estimated ones. The differences between measured and estimated values were related with thermal gradient around soil surface. The calculation suggested that the soil horizon above 10 cm depth would be a major CO2 source of soil respiration at about 70% in August and 40% in February. The depth of soil horizon in which soil microbial activity was significantly higher was not settled and would have a seasonal variation. (C) 2002 Elsevier Science B.V. All rights reserved.

We predicted the future condition of places in which major cereals are cultivated using satellite remote sensing data in combination with spatial analysis as provided by Geographical Information System (GIS). The Open Policy and Economic Reform have significantly accelerated economic growth of mainly in coastal regions of China. Economic growth in China will change the diet and will increase cereal demand. As a case study, we predicted the trend of major cereal production in China in the 21st century. We also predicted the agricultural productivity of existing fields cropped with major cereals based on climatic indices, i.e., the net primary productivity (NPP) and the balance between precipitation and potential evapotranspiration (P-PET), from the present to 2090s. The agricultural productivity in Central, Northern and Northeastern China will increase as a result of an increase in the NPP, while that in Southern China will not. The agricultural productivity in Western China will decline as a result of a decrease in P-PET. Desertification will be a problem in these regions. The result of this study suggests that combined analysis of satellite remote sensing and GIS data is effective for use in a comparative study of the present and the future conditions.

Cyperus esculentus is an exotic clonal (or pseudoannual) weed in Japan, and its range is steadily increasing. To investigate its interclonal variation and phenotypic plasticity in response to water availability, five clones of C. esculentus, collected from different sites in Japan, were grown singly in pots placed outdoors under dry and wet conditions. All the traits examined showed considerable variation among the five clones. However, two clones from Tochigi were similar to each other
thus, they might have originated from the same founder population. The clone from Ibaraki was quite different from the others. Therefore, it is suggested that the Japanese populations of C. esculentus might have resulted from multiple introductions of genotypes from geographically separated and, hence, genetically differentiated, source populations. All the clones also showed considerable plasticity in response to water availability. Clones with a larger ramet number had a greater plasticity, whereas tuber size was invariant across water treatments. Highly plastic traits had generally low interclonal variation in plasticity. All the clones had high productivity and produced more ramets and tubers under wet conditions than under dry conditions. Moreover, water availability could partially regulate the mode of its reproduction
wet conditions favored tuber production (vegetative propagation) while dry conditions favored sexual reproduction. A number of trade-offs occurred between the traits of clonal growth, storage and sexual reproduction, indicating that allocation among the competing functions/organs is mutually exclusive in plants. The results obtained here suggest that C. esculentus is more likely to invade wet habitats than dry habitats.

This paper describes the impacts of climate change on major cereal crops, i.e., rice, wheat and maize, in Asia using results from simulation studies using the CCSR (Center for Climate System Research, the University of Tokyo) GCM (Global Climate Model). Climate change could cause the air temperature and precipitation to become unsuitable for major cereal cultivation in Asia. To predict the impacts of climate change on sustainable cropping and agricultural development, we evaluated changes in climatic conditions in Asia in major cereal cropping seasons. We used the monthly average temperature and precipitation data from the 1961-90 mean climatology and CCSR GCM. We analyzed changes in the mean temperature and precipitation of major cereal-producing regions at different cropping seasons from the present through the 2090s. Climate change will generally increase both the air temperature and precipitation in Asia. The impacts of climate change have two sides: plus and minus. The increase in the air temperature and precipitation will be disadvantageous to wheat cropping in Asia. In contrast, some regions in subtropical Asia will have excess water resources during the rainy season. Precautionary measures against flooding will be needed in these areas.

CO and H-2 uptake by soil was studied as a diffusion process. A diffusion model was used to determine how the surface fluxes (net deposition velocities) were controlled by in-situ microbial uptake rates and soil gas diffusivity calculated from the 3-phase system (solid, liquid, gas) in the soil. Analytical solutions of the diffusion model assuming vertical uniformity of soil properties showed that physical properties such as air-filled porosity and soil gas diffusivity were more important in the uptake process than in the emission process. To incorporate the distribution of in-situ microbial uptake, we used a 2-layer model incorporating "a microbiologically inactive layer and an active layer" as suggested from experimental results. By numerical simulation using the 7-layer node, we estimated the effect of several factors on deposition velocities. The variations in soil gas diffusivity due to physical properties, i.e., soil moisture and air-filled porosity, as well as to the depth of the inactive layer and in-situ microbial uptake, were found to be important in controlling deposition velocities. Tills result shows that the diffusion process in soil is critically important for CO and H-2 uptake by soil, at least in soils with higher in-situ uptake rates and/or with large variation in soil moisture. Similar uptake rates and the difference in deposition velocity between CO and H-2 may be attributable to differences in CO and H-2 molecular diffusivity. The inactive layer is resistant to diffusion and creates uptake limits in CO and H-2, by soil. The coupling of high temperature and a thick inactive layer, common in arid soils, markedly lowers net CO deposition velocity. The temperature for maximum uptake of CO changes with depth of the inactive layer.

A two-layered diffusion model was applied to the uptake process of trace gases as CO, H-2, and CH4 which are utilized by soil microorganisms or enzymes assuming that its uptake obey first-order kinetics about its concentration. Analytical solutions for mono-layered model,exhibit that the physical property as gas diffusivity in soil is more important for uptake process than emission process. The numerical simulation shows that the deposition of CO, H-2, and CH4 are limited by the combination of transport process and the localization of the soil uptake zone, within 0.06cm s(-1) for CO and 0.1cm s(-1) for H-2, respectively, which are in reasonable consistence with field measurements.

To investigate whether wind is a significant driving force in the diffusion of CO and CH4 from the atmosphere into soil, we measured the concentrations of these two gases at two heights above a temperate grass field in Japan and estimated their deposition velocities using micrometeorological techniques. The concentrations were inversely correlated with wind speed, indicating that the local concentrations were influenced by ground sources. The CO and CH4 concentrations at 0.33 m were usually lower than those at 1.3 m. Although nocturnal data are suspected to be non-stationary, by selecting several periods when the changes of the concentrations were small but larger than analytical precision, we obtained a CO velocity of 2.9 and 3.9 x 10(-2) cm s(-1), agreeing with a CO deposition velocity, 3.4 x 10(-2) cm s(-1), obtained by applying a method using CO2 as a tracer. The CH4 influx obtained by the method using CO2 as a tracer was 13 ng m(-2) s(-1). The ranges of the CO deposition velocity and CH4 influx were similar to those obtained in previous studies in grassfields and in a nearby arable field using a closed-chamber technique. This shows that light winds do not greatly accelerate CO and CHS uptake by soil. (C) 2000 Elsevier Science Ltd. All rights reserved.

A neighbourhood-based competition model for plant individuals is studied to evaluate how a hierarchical structure related to size may emerge in plant communities. It is shown by numerical simulations and linear stability analysis that many stable states exist in the hierarchical structure when both the total number of individuals and the degree of asymmetry of competition are high. When the hierarchical structures are self-organized by the dynamic instability of the homogeneous state due to non-linearity of competition, it is proved that these states are always locally stable. The relevance of the results to size structures in real plant communities (boreal forests vs tropical and temperate forests) is discussed. This is suggested to be the mechanism responsible for the coexistence of species in plant communities. (C) 1999 Society for Mathematical Biology.

A local coupling model for growth dynamics of plant populations is proposed, in which each individual occupies a square lattice point and follows logistic growth with potential maximum relative growth rate c(0) being reduced by the competitive effects from eight interacting neighbours. The competitive effects are given by the competition function W, which describes the degree of competitive asymmetry between individuals, beta and the distance-dependent intensity of competition between individual i and j, alpha(ij), as a coupling constant. Then a global coupling model corresponding to the local coupling model is proposed, in which every individual interacts with all the other individuals in the field. In the global coupling model, the coupling constant alpha as the same parameter for all the individuals was employed instead of alpha(ij)'s in the local coupling model. alpha was given as an average of alpha(ij)'s of eight interacting neighbours in the local coupling model. Both the coupling constants were normalized by the number of interacting individuals to make simulation results comparable between the models. Numerical simulations revealed that the local coupling model can be simulated by the corresponding global coupling model fairly well if population growth dynamics are continuous without deaths or new recruits. In both the models, multi-layered structure of size distribution was more likely to emerge under asymmetric and/or intense competition than under symmetric and/or weak competition. This conforms to the widely known phenomenon of size-structure dynamics in plant populations. Since theoretical analysis is impossible for the local coupling model, linear stability analysis of size-structure dynamics was made for the global coupling model. It was theoretically shown that if alpha &lt; c(0)/(1 + beta), mono-layered size structure is stable; if alpha &gt; c(0)/(1 + beta), multi-layered size structure is stable. As alpha and/or beta increases (decreases), multi-layered (mono-layered) size structure gets stable. As c(0) (i.e. seedlings' relative growth rate) increases, the domain of stable mono-layered (multi-layered) size structure becomes larger (smaller). Therefore, the above simulation results were supported by linear stability analysis of the dynamical systems. Ecological implications of these theoretical results are discussed concerning the relationship between the stability of stand size structure and the degree of competitive asymmetry (multi-layered versus mono-layered). (C) 1999 Elsevier Science B.V. All rights reserved.

The effects of competition between individuals (the degree of competitive asymmetry and the intensity of competition; an endogenous biological factor) and the intensity of disturbance (unidirectional prevailing winds in the present paper; an exogenous environmental factor) on the spatial pattern dynamics of wave regeneration in subalpine regions were studied by using a coupled map lattice model (a lattice model with discrete time, discrete space and continuous state) of plant populations. It was shown by simulation that the wave-shaped spatial pattern of regeneration is more likely to emerge under symmetric competition than under asymmetric competition and that the likelihood of emergence increases with the intensity of disturbance, It was already shown both theoretically and by field data analysis that Abies undergoes symmetric competition and that Betula undergoes asymmetric competition. Therefore, a theoretical explanation can be given as to why the wave-shaped spatial pattern occurs in Abies forests but not in Betula forests although both the species occur in subalpine regions. Our theoretical study showed that the interaction between endogenous and exogenous factors determine the spatial pattern dynamics in relation to the regeneration of plant communities. (C) 1999 Elsevier Science B.V. All rights reserved.

The properties of a semiconductor ammonia odor sensor were investigated in the laboratory and field. The laboratory results showed that the ammonia sensor was sensitive to water vapor pressure as well as ammonia concentration. In the field, ammonia concentration was calculated from the ammonia odor sensor values and the water vapor pressure. The calibrated ammonia concentration had a good relationship (R=0.80, n=57) with ammonia concentration by an acid-trap calorimetry method, where the ranges of ammonia concentration and water vapor pressure were -100 ppbv and 4-6 hpa, respectively. The ammonia odor sensor can measure concentrations of ammonia as 10 ppbv, which is the background level in agricultural fields, with an estimated error of ±9.72 ppbv.

To explore how a stable size hierarchical structure may emerge in plant communities, a simple competition model for plant individuals with two types of competition mode, asymmetric and symmetric, is studied numerically and theoretically. The model incorporates size-ratio dependent interactions between individuals, which reflects the sensitivity of resource gain to differences in individual biomass at the seedling stage. From numerical simulations, the stability of the multi-layered size structure is determined by the relationships between the number of population, the degree of competitive asymmetry and the potential maximum relative growth rate (i.e. seedling's relative growth rate) as follows: (1) As the number of population and/or the degree of competitive asymmetry increases [decreases], multi-layered [mono-layered] size structure becomes stable; (2) As the potential maximum relative growth rate increases, the domain of stable mono-layered [multilayered] size structure becomes larger [smaller]. It is further shown that, when the potential maximum relative growth rate is fixed, (3) multi-layered size structure is more likely to be stable under asymmetric competition than under symmetric competition; (4) mono-layered size structure is more likely to be stable under symmetric competition than under asymmetric competition. When the hierarchical structures are self-organized by the dynamic instability of the homogeneous state due to nonlinearity of competition, it is proved by theoretical analysis that these states are always locally stable. This is also suggestive of the mechanisms of species coexistence in plant communities.

The effects of the mode of competition between individual plants (symmetric versus asymmetric) and the gap formation caused by natural disturbances on the dynamics of spatial configuration pattern of individual size were investigated theoretically based on an individual growth model incorporating competitive effects of neighbouring individuals. The degree of spatial heterogeneity in local size distribution was represented by the CV (coefficient of variation) of averages of local size distributions (CVav), the average of CVs of local size distributions (AV(cv)) and the CV of CVs of local size distributions (CVcv). Without gap formation, CVav, AV(cv), and CVcv were larger under asymmetric competition than under symmetric one, suggesting a fine-scale mosaic spatial pattern in asymmetrically competing populations. With gap formation, these statistics under symmetric competition approached those values under asymmetric competition. The spatial configuration pattern of individual size also showed the same trend in terms of the semivariogram. The patchiness index was almost the same in both gap and non-gap cases irrespective of the mode of competition. The semivariogram and patchiness index showed the presence of more uniform and larger patches under symmetric competition than asymmetric competition. Gap formation therefore increased spatial heterogeneity in local size distribution especially under symmetric competition, but there was still a difference in spatial heterogeneity between the two modes of competition even in the gap formation case. The effects of gap formation on spatial pattern dynamics were larger under symmetric competition than under asymmetric competition; under asymmetric competition, the spatial pattern dynamics were similar in both gap and son-gap cases. Therefore, against spatial disturbances (i.e. gap formation), symmetric competition brings about a more variable system than asymmetric competition. These theoretical results can explain spatial pattern dynamics of natural forests (northern coniferous and temperate hardwood forests). In conclusion, both the disturbance regime (gap formation process) and the mode of competition between individuals should be investigated to study the spatial pattern dynamics and species diversity of plant communities. The implications of the mode of between-individual competition for conservation biology are discussed. It is suggested that symmetrically competing plant communities (e.g. northern coniferous forests) should be preserved in larger areas than asymmetrically competing ones (e.g. temperate hardwood forests) if the plant communities are subject to frequent natural disturbances. (C) 1998 Elsevier Science B.V. All rights reserved.

We estimate the time change of the rice production for every 10 years for a period of 100 years by using the MRI's CGCM containing the transient CO, experiment,(12) and the crop model named SIMRIW.(5) On this estimation by these models, we choose the 'Koshihikari' as a representative cultivar and assume optimal cultivation technologies are adopted.
Annual and summer seasonis mean air temperature increase almost linearly, and become 2.0 to 3.0 degree C higher after 70 years, 3.0 to 4.0 degree C higher after 100 years, and these temperature changes are almost 1.0 to 2.0 degree C smaller than the predictions of previous studies.(1). (7). (14) AS a result of this, the cultivated area of 'Koshihikari' is estimated to spread northward, and the yield in currently cultivated area is also simulated not to change toward a harsh condition for at least 100 years, under optimal cultivation technologies.

Economic, political, environmental and agricultural variables were incorporated into a model in order to predict food supply and demand in the 21st century. Market forces do not apply to agricultural products in domestic market and price is determined politically. Invisible hand are used in order to increase prices of agricultural products. Population, area of arable land and income in the agricultural sector are key factors which determine cereal prices. Scenario analysis using a numerical model was carried out to predict cereal prices in the People's Republic of China (PRC) in the 21st century. The analysis shows that cereal prices will increase in the PRC rapidly if the country continues it's high rate of economic growth. The Chinese government have announced that they will continue the policy of self-sufficiency for food, but importing cheap cereal from international markets would be attractive an option for providing low price food to the people living in cities.

The effects of the mode of competition between individual plants (symmetric vs. asymmetric) and the gap formation caused by natural disturbances on the dynamics of spatial configuration size pattern of individuals were investigated theoretically based on an individual growth model incorporating competitive effects of neighbouring individuals. The degree of spatial heterogeneity in local size distribution was represented by the CV of averages of local size distributions (CVav) and the average of CVs of local size distributions (AVcv). Without gap formation, both the CVav and AVcv were larger under asymmetric competition than under the symmetric one, suggesting a fine-scale mosaic spatial pattern in the asymmetrically competing population. With gap formation, the CVav and AVcv, under symmetric competition, approached those values under asymmetric competition. Gap formation therefore increased spatial heterogeneity in local size distribution especially under symmetric competition, but there was still a difference in spatial heterogeneity between the two modes of competition even in the gap formation case. The effects of gap formation on spatial pattern dynamics were larger under symmetric competition than under asymmetric one; under the latter, the spatial pattern dynamics were similar in both gap and non-gap cases. Therefore, against spatial disturbances (i,e. gap formation), symmetric competition produces a more variable system than asymmetric competition. Both the disturbance regime (gap formation process) and the mode of competition between individuals should be investigated to study the spatial pattern dynamics and species diversity of plant communities. The implications of the mode of between-individual competition for conservation biology are discussed. It is suggested that symmetrically competing plant communities should be preserved in larger areas than asymmetrically competing ones if plant communities are subject to frequent natural disturbances.

The objectives of this study are the development of a newly designed weather generator for daily mean air temperature and a description of its performance. Sequentially generated data of the temperature respond to occurrence frequencies of the pressure patterns in East Asia. The generator is effective as a practical and conjunctive model between GCM and crop productivity in the field. © 1997, The Society of Agricultural Meteorology of Japan. All rights reserved.

It is thought that an increase in cereal production will eventually reach to a saturation point due to the fact that the area of global arable land has been almost unchanged since 1960. However, it is predicted that the demand for cereals will increase as the global population increases. It would be necessary for Japan to constantly monitor food production both in Japan and foreign countries and to secure necessary food supplies. The aim of this study is to predict the change in area and productivity of cereal cultivation that could be caused by global warming for a 50-100 year period in order to take measures to secure future food supplies. For prediction of global changes in the planted area and cereal production in longterm view caused by global warming over a 50-100 year period, we propose a method for the identification of suitable/potential regions for major cereal cultivation using satellite remote sensing data, weather (atmospheric temperature and precipitation) data and soil data. This method detects agricultural land-use locations using satellite remote sensing data, and then extracts suitable/potential regions for major cereal cultivation which meet the required conditions, i.e. the soil property, the temperature requirement and the moisture requirement. We applied this method to predict how the area of suitable/potential regions for major cereal cultivation globally would change under conditions of double the current CO2 concentration. We estimated the present area of suitable regions for major cereal cultivation to be 515 Mha (Million ha). Under conditions of double the current CO2 concentration, it was estimated that the area of suitable regions would be 279 Mha which represents a decrease of 46%. We estimated the present area of potential regions for major cereal cultivation to be 1, 333 Mha. We predicted that the area of potential regions under conditions of double the current CO2 concentration would be 1, 378 Mha, representing an increase of 3% over that of the area of present potential regions for major cereal cultivation. Using above results and the NPP (Net Primary Productivity), change in cereal production was estimated. It was predicted that the total NPP in the top 6 countries of cereal production would increase 70% caused by global warming. According to the prediction of change in NPP, it is unlikely that global warming results in the food shortage in the globe. © 1997, JAPAN SECTION OF THE REGIONAL SCIENCE ASSOCIATION INTERNATIONAL. All rights reserved.

The relationships between crown architecture and species coexistence were studied using the diffusion model and the canopy photosynthesis model for multi-species plant communities. The present paper deals with two species having different crown shapes [conic-canopy plant (CCP) and spheroidal-canopy plant (SCP)], for various initial mean sizes at the establishment stage and physiological parameter Values (photosynthetic rate, etc.). Recruitment processes were not incorporated into the model, and thus simulations were made for the effects on the pattern of species coexistence of either sapling competition starting from different sapling banks or competition in single-cohort stands with little continual establishment of species until a stand-replacement disturbance. The following predictions were derived: (1) SCPs can establish later/slowly in the lower canopy layer even if they are overtopped by a CCP which established first/rapidly; (2) if SCPs established first/rapidly and occupy the upper canopy layer, a CCP can rarely establish later/slowly in the lower canopy layer; (3) smallest-sized CCPs can persist well in the lowermost canopy layer overtopped by a SCP, suggesting a waiting strategy of CCP's saplings in the understorey of a crowded stand; (4) even if CCPs established first/rapidly and occupy the upper canopy layer, an SCP can establish later/slowly in the lower canopy layer. Therefore, the species diversity of SCPs which established first/rapidly and occupy the upper canopy layer limits the number of CCP species which can establish later/slowly. In contrast, the species diversity of CCPs which established first/rapidly and occupy the upper canopy layer does not affect the number of SCP species which can establish later/slowly. The combination of initial sizes of a CCP and,an SCP at the establishment stage (i.e. establishment timing) affects the segregation of vertical positions in the canopy between the two species with different crown shape, and not only species-specific physiological traits but also crown architecture greatly affects the coexistence pattern between species with different crown architectures. The theoretical predictions obtained here can explain coexistence patterns found in single-cohort conifer-hardwood boreal and sub-boreal forests, pointing to the significance of crown architecture for species coexistence. (C) 1996 Annals of Botany Company

The relationships between vertical foliage profile of an individual plant, competition between individuals, size structure and allocation pattern between stem diameter (D) and plant height (H) were investigated using canopy photosynthesis and two-dimensional continuity equation models including D and H as two independent variables. Broad-leaved type plants (more foliage mass in the upper layer than in the lower layer of the canopy of an individual when grown in isolation) showed curvilinear D-H relationship and bimodal H distribution, and underwent more asymmetric competition than coniferous type plants (more foliage mass in the lower layer than in the upper layer of the canopy of an individual when grown in isolation) under crowded conditions. Coniferous type plants showed almost linear D-X relationship (i.e. simple allometry) and unimodal H distribution, and underwent more symmetric competition than broad-leaved type plants under crowded conditions. However, in both the cases D distributions were unimodal. Allocation patterns between D and H affected these features only a little. These simulation results can explain many actual data already published. The value of eta for an individual plant (foliage profile parameter of an individual canopy representing a species-specific branching pattern and canopy morphology when grown in isolation) governs size structure (bimodal or unimodal), the mode of competition, D-H relationship and mean D-mean H trajectory with time under crowded conditions. Therefore, a simple view of the competition-allometry relationship that competition determines allometry should be re-evaluated incorporating the foliage profile of an individual. These theoretical results should also be important when studying species coexistence. The canopy tends to be multi-layered in broad-leaved type plants and mono-layered in coniferous type plants. Therefore, it is hypothesized that species coexistence in the former is mainly by way of separation of vertical space (i.e. niche separation under strongly asymmetric competition) and that species coexistence in the latter is due to nearly symmetric competition in a single canopy layer. (C) 1995 Annals of Botany Company

A dynamic model for growth and mortality of individual plants in a stand is developed, based on the process of canopy photosynthesis, and assuming an allometric relationship between plant height and weight, i.e. an allocation-growth pattern of plant height and stem diameter. The following were shown by simulation: (i) competition between individuals in a crowded stand is never completely one-sided but always asymmetrically two-sided, even though competition is only for light; (ii) plants of the 'height-growth' type exhibit a greater asymmetry in competition than plants of the 'diameter-growth' type; (iii) the competition mode between plants of the 'height-growth' type becomes more asymmetric with increasing photosynthetic ability than with plants of the 'diameter-growth' type. These results can explain recent empirical results obtained from several natural plant communities.