Toshichika Iizumi

Impacts of El Niño Southern Oscillation on the global yields of major crops

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 Niño/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 Niño 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 Niña years tend to be below normal (-4.5 to 0.0%). Our findings highlight the importance of ENSO to global crop production.

Soil organic carbon sequestration in upland soils of northern China under variable fertilizer management and climate change scenarios

We determined the historical change in soil organic carbon (SOC) stocks from long-term field trials that represent major soil types and climatic conditions of northern China. Soil carbon and general circulation models were validated using these field trial data sets. We then applied these models to predict future change in SOC stocks to 2100 using two net primary production (NPP) scenarios (i.e., current NPP or 1% year−1 NPP increase). The conversion rate of plant residues to SOC was higher in single-cropping sites than in double-cropping sites. The prediction of future SOC sequestration potential indicated that these soils will be a net source of carbon dioxide (CO2) under no fertilizer inputs. Even when inorganic nutrients were applied, the additional carbon input from increased plant residues could not meet the depletion of SOC in parts of northern China. Manure or straw application could however improve the SOC sequestration potential at all sites. The SOC sequestration potential in northern China was estimated to be −4.3 to 18.2 t C ha−1 by 2100. The effect of projected climate change on the annual rate of SOC change did not differ significantly between climate scenarios. The average annual rate of SOC change under current and increased NPP scenarios (at 850 ppm CO2) was approximately 0.136 t C ha−1 yr−1 in northern China. These findings highlight the need to maintain, and where possible increase, organic carbon inputs into these farming systems which are rapidly becoming inorganic fertilizer intensive.

ELPIS-JP: a dataset of local-scale daily climate change scenarios for Japan

We developed a dataset of local-scale daily climate change scenarios for Japan (called ELPIS-JP) using the stochastic weather generators (WGs) LARS-WG and, in part, WXGEN. The ELPIS-JP dataset is based on the observed (or estimated) daily weather data for seven climatic variables (daily mean, maximum and minimum temperatures; precipitation; solar radiation; relative humidity; and wind speed) at 938 sites in Japan and climate projections from the multi-model ensemble of global climate models (GCMs) used in the coupled model intercomparison project (CMIP3) and multi-model ensemble of regional climate models form the Japanese downscaling project (called S-5-3). The capability of the WGs to reproduce the statistical features of the observed data for the period 1981–2000 is assessed using several statistical tests and quantile–quantile plots. Overall performance of the WGs was good. The ELPIS-JP dataset consists of two types of daily data: (i) the transient scenarios throughout the twenty-first century using projections from 10 CMIP3 GCMs under three emission scenarios (A1B, A2 and B1) and (ii) the time-slice scenarios for the period 2081–2100 using projections from three S-5-3 regional climate models. The ELPIS-JP dataset is designed to be used in conjunction with process-based impact models (e.g. crop models) for assessment, not only the impacts of mean climate change but also the impacts of changes in climate variability, wet/dry spells and extreme events, as well as the uncertainty of future impacts associated with climate models and emission scenarios. The ELPIS-JP offers an excellent platform for probabilistic assessment of climate change impacts and potential adaptation at a local scale in Japan.

How much has the increase in atmospheric CO2 directly affected past soybean 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.

Changes in yield variability of major crops for 1981–2010 explained by climate change

While changes in temperature and precipitation extremes are evident, their influence on crop yield variability remains unclear. Here we present a global analysis detecting yield variability change and attributing it to recent climate change using spatially-explicit global data sets of historical yields and an agro-climatic index based on daily weather data. The agro-climatic index used here is the sum of effective global radiation intercepted by the crop canopy during the yield formation stage that includes thresholds for extreme temperatures and extreme soil moisture deficit. Results show that year-to-year variations in yields of maize, soybean, rice and wheat in 1981–2010 significantly decreased in 19%–33% of the global harvested area with varying extent of area by crop. However, in 9%–22% of harvested area, significant increase in yield variability was detected. Major crop-producing regions with increased yield variability include maize and soybean in Argentina and Northeast China, rice in Indonesia and Southern China, and wheat in Australia, France and Ukraine. Examples of relatively food-insecure regions with increased yield variability are maize in Kenya and Tanzania and rice in Bangladesh and Myanmar. On a global scale, over 21% of the yield variability change could be explained by the change in variability of the agro-climatic index. More specifically, the change in variability of temperatures exceeding the optimal range for yield formation was more important in explaining the yield variability change than other abiotic stresses, such as temperature below the optimal range for yield formation and soil water deficit. Our findings show that while a decrease in yield variability is the main trend worldwide across crops, yields in some regions of the world have become more unstable, suggesting the need for long-term global yield monitoring and a better understanding of the contributions of technology, management, policy and climate to ongoing yield variability change.

Diagnostics of Climate Model Biases in Summer Temperature and Warm-Season Insolation for the Simulation of Regional Paddy Rice Yield in Japan

Abstract 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 simulat...

Climate Change Impact on Rice Insurance Payouts in Japan

Abstract 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 shorten...

Dependency of parameter values of a crop model on the spatial scale of simulation

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.

A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison

The Global Risk Assessment toward Stable Production of Food (GRASP) project uses global crop models to evaluate the impacts on global food security by changes in climate extremes, water resources, and land use. Such models require meteorological forcing data. This study presents the development of the GRASP forcing data that is a hybrid of the reanalyses (ERA-40 and JRA-25) and observations. The GRASP data offer daily mean, maximum, and minimum 2 m air temperatures as well as precipitation, solar radiation, vapor pressure, and 10 m wind speed over global land areas, excluding Antarctica, for the period 1961–2010 at a grid size of 1.125°. The monthly climatologies of the variables of the GRASP data were forced to be close to those of the observations for the baseline period (1961–1990 or 1983–2005) through bias corrections. The GRASP data are intercompared with other forcing data for land surface modeling (the S06, WATCH Forcing Data, and WATCH Forcing Data Methodology Applied to ERA-Interim data). The results demonstrate that the daily minimum temperature, diurnal temperature range, vapor pressure, solar radiation, and wind speed from the GRASP data are more valuable for crop modeling than the reanalyses and other forcing data. For remaining variables, the reliability of the GRASP data is higher than that of the reanalyses and on a similar level with that of the other forcing data. The GRASP data offer accurate estimates of daily weather as the inputs for crop models, providing unique opportunities to link historical changes in climate with crop production over the last half century.

Estimation of the damage area due to tropical cyclones using fragility curves for paddy rice in Japan

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.