Modeling the multiple effects of temperature and radiation on rice quality

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

Impacts of El Nino 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 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 2.15.4 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

Uncertainty in land-use adaptation persists despite crop model projections showing lower impacts under high warming

Climate change is expected to impact crop yields and alter resource availability. However, the understanding of the potential of agricultural land-use adaptation and its costs under climate warming is limited. Here, we use a global land system model to assess land-use-based adaptation and its cost under a set of crop model projections, including CO2 fertilization, based on climate model outputs. In our simulations of a low-emissions scenario, the land system responds through slight changes in cropland area in 2100, with costs close to zero. For a high emissions scenario and impacts uncertainty, the response tends toward cropland area changes and investments in technology, with average adaptation costs between −1.5 and +19 US$05 per ton of dry matter per year. Land-use adaptation can reduce adverse climate effects and use favorable changes, like local gains in crop yields. However, variance among high-emissions impact projections creates challenges for effective adaptation planning

Future change of daily precipitation indices in Japan: a stochastic weather generator-based bootstrap approach to provide probabilistic climate information

This study proposes the stochastic weather generator (WG)-based bootstrap approach to provide the probabilistic climate change information on mean precipitation as well as extremes, which applies a WG (i.e., LARS-WG) to daily precipitation under the present-day and future climate conditions derived from dynamical and statistical downscaling models. Additionally, the study intercompares the precipitation change scenarios derived from the multimodel ensemble for Japan focusing on five precipitation indices (mean precipitation, MEA; number of wet days, FRE; mean precipitation amount per wet day, INT; maximum number of consecutive dry days, CDD; and 90th percentile value of daily precipitation amount in wet days, Q90). Three regional climate models (RCMs: NHRCM, NRAMS and TWRF) are nested into the high-resolution atmosphere-ocean coupled general circulation model (MIROC3.2HI AOGCM) for A1B emission scenario. LARS-WG is validated and used to generate 2000 years of daily precipitation from sets of grid-specific parameters derived from the 20-year simulations from the RCMs and statistical downscaling model (SDM: CDFDM). Then 100 samples of the 20-year of continuous precipitation series are resampled, and mean values of precipitation indices are computed, which represents the randomness inherent in daily precipitation data. Based on these samples, the probabilities of change in the indices and the joint occurrence probability of extremes (CDD and Q90) are computed. High probabilities are found for the increases in heavy precipitation amount in spring and summer and elongated consecutive dry days in winter over Japan in the period 2081-2100, relative to 1981-2000. The joint probability increases in most areas throughout the year, suggesting higher potential risk of droughts and excess water-related disasters (e. g., floods) in a 20 year period in the future. The proposed approach offers more flexible way in estimating probabilities of multiple types of precipitation extremes including their joint probability compared to conventional approaches

Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications

Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark

Global dataset of historical yields v1.2 and v1.3 aligned version

The Global Dataset of Historical Yield (GDHYv1.2+v1.3) offers annual time series data of 0.5-degree grid-cell yield estimates of major crops worldwide for the period 1981-2016. The crops considered in this dataset are maize, rice, wheat and soybean. The unit of yield data is t/ha. The grd-cell yield data were estimated using the satellite-derived crop-specific vegetation index and FAO-reported country yield statistics. Maize and rice have the data for each of two growing seasons (major/secondary). "Winter" and "spring" are used as the growing season categories for wheat. Only "major" growing season is available for soybean. These growing season categories are based on Sacks et al. (2010, DOI: 10.1111/j.1466-8238.2010.00551.x). The geographic distribution of harvested area changes with time in reality, but we used the time-constant data in 2000 (Monfreda et al., 2008, doi:10.1029/2007GB002947). Many missing values are found in the first (1981) and last (2016) years because grid-cell yields are not estimated for these years when growing season spans two calendar years. The data for the period 1981-2010 are the same with the version 1.2 ( https://doi.org/10.20783/DIAS.528). For the period 2011-2016, a newly created version 1.3 using the satellite products that are different with earlier versions was alighned to ensure the continuity of yield time series. This version is therefore called "the alighned version v1.2+v1.3"

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

How do weather and climate influence cropping area and intensity?

AbstractMost studies of the influence of weather and climate on food production have examined the influence on crop yields. However, climate influences all components of crop production, includes cropping area (area planted or harvested) and cropping intensity (number of crops grown within a year). Although yield increases have predominantly contributed to increased crop production over the recent decades, increased cropping area as well as increases in cropping intensity, especially in the tropics, have played a substantial role. Therefore, we need to consider these important aspects of production to get a more complete understanding of the future impacts of climate change. This article reviews available evidence on how climate might influence these under-studied components of crop production. We also discuss how farmer decision making and technology might modulate the production response to climate. We conclude by discussing important knowledge gaps that need to be addressed in future research and potential ways for moving forward

Climate mitigation sustains agricultural research and development expenditure returns for maize yield improvement in developing countries

Governmental expenditure on agricultural research and development (R&D) has played a substantial role in increasing crop yields in recent decades. However, studies suggest that annual yield growth rates would decline in a warming climate compared to that in a non-warming climate. Here, we present how projected climate could alter maize yield gain owing to a US$ 1 billion increase in agricultural R&D expenditure (referred to as yield response) for 71 maize-producing countries using global gridded crop model simulations with socioeconomic and climate scenarios as inputs. For the middle of this century (2041–2060) under the low warming scenario (shared socioeconomic pathways: SSP126), the median yield response between countries is estimated to be the highest at 27.2% in the low-income group, followed by 6.6% in the lower-middle-income group, 1.0% in the high-income group, and 0.1% in upper-middle-income group. The projected median yield response for lower (the low- and lower-middle)-income groups under the high warming scenario (SSP585) was approximately half than that under the low warming scenario: 27.2% → 15.6% for the low-income, 6.6% → 1.7% for the lower-middle-income, and 1.0% → 0.6% for the high-income groups. For the upper-middle-income group, where there is limited room for adopting high-yielding technology and management already being used in higher (the high- and higher-middle)-income groups, the negative impacts of climate change cannot be offset and yields are projected to decline, even with continued R&D investments (0.1% → –0.2%). Even if the R&D expenditures increase at the same value, expected yield gains will depend on future warming levels. This finding suggests that climate mitigation is a prerequisite for maintaining the yield returns from agricultural R&D investments in developing countries