Crop Environment Resource Synthesis Research Articles

Evaluating the effects of the CERES-Rice model to simulate upland rice (Oryza sativa L.) yield under different plant density and nitrogen management strategies in Fogera Plain, Northwest Ethiopia

This study assessed the optimal nitrogen (N) fertilizer rate and planting density for the well-adapted upland rice cultivar NERICA_4 on the Fogera Plain. The primary objective was to evaluate the effects of varied planting densities and N-fertilizer rates on upland rice yield and other agronomic parameters. A two-year field study (2020 and 2021) was conducted at the Fogera Rice Research Field Station, testing nine plant densities (75, 87, and 98; 72, 82, and 91; 70, 79, and 89 plants per m2 and two N rates (115 and 138 kg N ha−1). The Crop Simulation Model Crop Environment Resource Synthesis (CSM-CERES-Rice) within the Decision Support System for Agrotechnology Transfer (DSSAT) framework was calibrated and validated using site-specific weather, soil, crop, and agronomic management data from the experiment. Results on the subsequent RMSE, RMSEn, and d index values during the calibration phase were 0.074 t ha−1, 1.82 %, and 0.86 of grain yield; 0.307 t ha−1, 3.36 %, and 0.87 of by-product yield; 0.489 t ha−1, 3.74 %, and 0.79 of top dry biomass yield; and 0.28, 8.24 %, and 0.63 of leaf area index values, respectively. Whereas results on the corresponding RMSE, RMSEn, and d index values during the evaluation phase were: 0.58 t ha−1, 1.33 %, and 0.90 of grain yield; 0.69 t ha−1, 0.58 %, and 0.99 of by-product yield; 0.678 t ha−1, 4.36 %, and 0.67 of top dry biomass yield; and 0.75, 13.92 %, and 0.74 of leaf area index, respectively. The findings of the long-term simulation showed that a 23 % increase in grain yield was achieved with 138 kg N ha−1 and 87 plants per m2 of planting density, as compared to 115 kg N ha−1 and 75 plants per m2 of plant density. The recommended optimum plant density and N fertilizer rate were 138 kg N ha−1 with PD2 of plant density for upland rice production in the Fogera Plain.

Trade-offs between grain supply and soil conservation in the Grain for Green Program under changing climate: A case study in the Three Gorges Reservoir region

Understanding the trade-offs between ecological benefits and cost of grain supply caused by ecosystem restoration is essential for decision-making. Nevertheless, due to climate change, the benefits of ecosystem restoration and cost of grain supply change across various spatial locations, thereby complicating the trade-offs. Taking one of China's largest scale ecosystem restorations, the Grain for Green Program (GGP), as an example, this study used the Three Gorges Reservoir (TGR) region as the case study area and combined the crop environment resource synthesis (CERES)-Crop model, future land-use simulation (FLUS), and the revised universal soil loss equation (RUSLE) to simulate future grain supply and soil erosion during 2021–2050 under three climate change and socioeconomic development scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) in the TGR region. The results showed that: (1) Until 2050, the implementation of GGP would bring a large soil conservation benefit by reducing soil erosion of 2.47–5.68 million tons, at the cost of 130,277–660,279 tons decrease in grain production in the TGR region. (2) Under SSP5-8.5 climate change scenario with the highest rainfall in the future, the GGP would maintain the greatest soil conservation benefits, resulting in a total amount of soil erosion decrease by 2.55 to 5.68 million tons. (3) Trade-offs between benefit of reducing soil erosion and cost of grain supply vary considerably across income. Specifically, GGP benefits are greater under low-income and higher-emission scenarios, with significant gains in soil erosion control and less impact on grain supply. In contrast, in high-income and low-emission scenarios, the GGP results in less soil erosion control and greater impact on grain supply.

Modeling maize growth and nitrogen dynamics using CERES-Maize (DSSAT) under diverse nitrogen management options in a conservation agriculture-based maize-wheat system

Agricultural field experiments are costly and time-consuming, and often struggling to capture spatial and temporal variability. Mechanistic crop growth models offer a solution to understand intricate crop-soil-weather system, aiding farm-level management decisions throughout the growing season. The objective of this study was to calibrate and the Crop Environment Resource Synthesis CERES-Maize (DSSAT v 4.8) model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based maize system. The model was also used to investigate the relationship between, temperature, nitrate and ammoniacal concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on maize yields. Using field data from 2019 and 2020, the DSSAT-CERES-Maize model was calibrated for plant growth stages, leaf area index-LAI, biomass, and yield. Data from 2021 were used to evaluate the model's performance. The treatments consisted of four nitrogen management options, viz., N0 (without nitrogen), N150 (150 kg N/ha through urea), GS (Green seeker-based urea application) and USG (urea super granules @150kg N/ha) in two contrasting tillage systems, i.e., CA-based zero tillage-ZT and conventional tillage-CT. The model accurately simulated maize cultivar’s anthesis and physiological maturity, with observed value falling within 5% of the model’s predictions range. LAI predictions by the model aligned well with measured values (RMSE 0.57 and nRMSE 10.33%), with a 14.6% prediction error at 60 days. The simulated grain yields generally matched with measured values (with prediction error ranging from 0 to 3%), except for plots without nitrogen application, where the model overestimated yields by 9–16%. The study also demonstrated the model's ability to accurately capture soil nitrate–N levels (RMSE 12.63 kg/ha and nRMSE 12.84%). The study concludes that the DSSAT-CERES-Maize model accurately assessed the impacts of tillage and nitrogen management practices on maize crop’s growth, yield, and soil nitrogen dynamics. By providing reliable simulations during the growing season, this modelling approach can facilitate better planning and more efficient resource management. Future research should focus on expanding the model's capabilities and improving its predictions further.

Agronomic management response in maize (Zea mays L.) production across three agroecological zones of Kenya

AbstractMaize (Zea mays L.) productivity in Kenya has witnessed a decline attributed to the effects of climate change and biophysical constraints. The assessment of agronomic practices across agroecological zones (AEZs) is limited by inadequate data quality, hindering a precise evaluation of maize yield on a large scale. In this study, we employed the DSSAT‐CERES‐Maize crop model (where CERES is Crop Environment Resource Synthesis and DSSAT is Decision Support System for Agrotechnology Transfer) to investigate the impacts of different agronomic practices on maize yield across different AEZs in two counties of Kenya. The model was calibrated and evaluated with observed grain yield, biomass, leaf area index, phenology, and soil water content from 2‐year experiments. Remote sensing (RS) images derived from the Sentinel‐2 satellite were integrated to delineate maize areas, and the resulting information was merged with DSSAT‐CERES‐Maize yield simulations. This facilitated a comprehensive quantification of various agronomic measures at pixel scales. Evaluation of agronomic measures revealed that sowing dates and cultivar types significantly influenced maize yield across the AEZs. Notably, AEZ II and AEZ III exhibited elevated yields when implementing combined practices of early sowing and cultivar H614. The impacts of optimal management practices varied across the AEZs, resulting in yield increases of 81, 115, and 202 kg ha−1 in AEZ I, AEZ II, and AEZ III, respectively. This study underscores the potential of the CERES‐Maize model and high‐resolution RS data in estimating production at larger scales. Furthermore, this integrated approach holds promise for supporting agricultural decision‐making and designing optimal strategies to enhance productivity while accounting for site‐specific conditions.

Winter Wheat Drought Risk Assessment by Coupling Improved Moisture-Sensitive Crop Model and Gridded Vulnerability Curve

The crop drought risk assessment is an important basis for mitigating the effects of drought on crops. The study of drought using crop growth models is an integral part of agricultural drought risk research. The current Decision Support System for Agrotechnology Transfer (DSSAT) model is not sufficiently sensitive to moisture parameters when performing simulations, and most studies that conduct different scenario simulations to assess crop drought vulnerability are based on the site-scale. In this paper, we improved the moisture sensitivity of the Crop Environment Resource Synthesis System (CERES)-Wheat to improve the simulation accuracy of winter wheat under water stress, and then we assessed the drought intensity in the Beijing-Tianjin-Hebei region and constructed a gridded vulnerability curve. The grid vulnerability curves (1 km × 1 km) were quantitatively characterized using key points, and the drought risk distribution and zoning of winter wheat were evaluated under different return periods. The results show that the stress mechanism of coupled water and photosynthetic behavior improved the CERES-Wheat model. The accuracy of the modified model improved in terms of the above-ground biomass and yield compared with that before the modification, with increases of 20.39% and 11.45% in accuracy, respectively. The drought hazard intensity index of winter wheat in the study area from 1970 to 2019 exhibited a trend of high in the southwest and low in the southeast. The range of the multi-year average drought hazard intensity across the region was 0.29–0.61. There were some differences in the shape and characteristic covariates of the drought vulnerability curves among the different sub-zones. In terms of the cumulative loss rates, almost the entire region had a cumulative drought loss rate of 49.00–54.00%. Overall, the drought risk index decreased from west to east and from north to south under different return periods. This quantitative evaluation of the drought hazard intensity index provides a reference for agricultural drought risk evaluation.

Covering rice demand in Southern China under decreasing cropping intensities and considering multiple climate and population scenarios

In recent years, rice (Oryza sativa L.) cropping intensity has been decreasing in Southern China, characterized mainly by the gradual conversion of double rice to single rice. However, few studies have evaluated the balance between rice supply and demand under different climate, population, and cropping intensity scenarios in this region. Here, the Crop Environment Resource Synthesis (CERES)-Rice model was used to investigate the effects of climate change on rice yield in southern eight provinces. The future cultivation areas of single rice and double rice were predicted by linear regression equations based on current area trajectories and official policies. Additionally, the rice demand was determined by the predicted population. Results indicated that the conversion of double rice to single rice appeared in Anhui, Hubei, Hunan, and Jiangxi. The present rice cultivation areas in other provinces were very close to the minimum rice cultivation areas proposed by local governments. The early rice yield showed a significant upward trend under global warming. The yields of single rice and late rice exhibited an insignificant upward trend under a moderate warming scenario but a significant downward trend under an aggressive warming scenario. Interestingly, the yield sum of double rice was more resilient to climate change due to the higher and more stable yield. At the provincial level, in the cultivation area conversion experiments in this study, the rice supply could generally cover the demand in Anhui, Hubei, Hunan, and Jiangxi; the supply in the other four provinces was less than or just close to the demand, while the demand was gradually reduced. Moreover, the total rice supply of the eight provinces could meet the demand, except for the most populous scenario during 2081–2100, and the supply could be improved by adjusting the conversion rate of double rice to single rice. These results have implications for the adjustment of rice cropping intensity and are important for ensuring rice sustainable production and food security under future climate change.

Optimizing the sowing window for direct-seeded rice (Oryza sativa L.) considering high yield and methane emissions in Central China

CONTEXTElevated temperatures have had many adverse effects on rice (Oryza sativa L.) production. Methane (CH4) emissions from rice paddy fields can further increase global temperatures and in turn affect rice production. Many adaptations, such as shifting the sowing date, can help rice plants avoid the influences of high temperatures and reduce CH4 emissions by shortening the rice-growth duration. OBJECTIVEThis study explored the optimal sowing window for direct-seeded rice with the highest yield and lowest CH4 emissions. METHODSHere, we used the calibrated Crop Environment Resource Synthesis (CERES)-Rice model and a widely used CH4 estimation equation to determine the optimal sowing window under historical and future scenarios in Hubei Province, China. This province was divided into four zones, and three rice cultivars were selected. Numerical simulations were conducted for sowing dates at three-day intervals across the entire potential sowing window for each cultivar and zone. The simulated rice yields and estimated CH4 emissions for all sowing dates were compared to determine the optimal sowing window. RESULTS AND CONCLUSIONSOur results indicated that due to the lower emergence rate caused by low temperatures during the early stage of the potential sowing window, rice yield gradually increased with sowing date delay and tended to be stable, while CH4 emissions exhibited the opposite pattern. In this study, the optimal sowing window was determined to be when the lowest CH4 emissions and highest rice yields occurred simultaneously. Under heat stress, these two kinds of events often did not overlap, and the optimal sowing window was defined by the highest yields only. In addition, the current sowing date was mostly within the optimal sowing window during the historical period. The optimal sowing window appeared later under future scenarios than in the historical period, suggesting that the sowing date should be delayed in the future. SIGNIFICANCERice plants cultivated in optimal sowing windows can produce high yields and emit the least CH4. This study provides a better understanding of the effects of shifting the sowing date on food security and greenhouse gas emissions, ensuring sustainable rice production.

Application of the CERES-Rice Model for Rice Yield Gap Analysis

Aim: This study aimed to quantify and identify the trends in the yield gaps over the 15 years (2006-07 to 2020-21) in the Karimnagar district of Telangana State in India. The DSSAT v4.7.5 CERES-Rice model was used to calculate the potential yields and then yield gaps were calculated. For the yield gaps, linear and compound growth rates were determined.
 Results: (Crop Environment Resource Synthesis) CERES-Rice model has simulated the potential yields with the given weather and soil data of Karimnagar district in India. Yield gap I has recorded positive trend, whereas the yield gap II and total yield gaps have recorded negative trend. Linear and Compound growth rates for yield gap I, yield gap II and total yield gaps were calculated in which only yield gap I showed positive growth rates whereas yield gap II and total yield gaps showed negative growth rates. However, at 5% level, the growth rates in yield gap-II are significant.
 Conclusion: It has been observed that, in Karimnagar district of Telangana, the production of rice has increased during the period under study, however there are yield gaps in rice. Using the DSSAT (Decision-support system for Agro-technology Transfer) model, potential rice yields in the Karimnagar district were predicted. There was no much variation in the yield gap I and it was noticed negative trends in yield gap II and total yield gaps. These gaps are to be filled to boost the productivity.

Climate Change Effect on Water Use Efficiency under Selected Soil and Water Conservation Practices in the Ruzizi Catchment, Eastern D.R. Congo

Concerns have been raised on the effectiveness and sustainability of Soil and Water Conservation (SWC) practices as adaptation options to climate change and high intra– and inter–annual rainfall variabilities in eastern Democratic Republic of Congo (DRC). This study was conducted in the Ruzizi Plain, a dryland area, to assess the performance of maize (Zea mays L.) under two Representative Concentration Pathways (RCP 4.5 and 8.5) and two SWC practices (tied ridges and conventional tillage). The AgMIP’s Regional Integrated Assessment (RIA) approach was used to simulate Water Use Efficiency (WUE) under the Cropping System Model–Crop Environment Resource Synthesis (CSM–CERES–Maize) of the Decision Support System for Agro–technology Transfer (DSSAT). The model was calibrated using experimental data from nine cropping seasons (2011–2018) and 100 farms. The model sensitivity was assessed as a function of temperature, water, and SWC practices for the same environments. Initial conditions of crop management practices were used as input data for CSM–CERES–Maize. Current climate data were extracted from AgMERRA datasets corrected with local data for the period of 1980 to 2021. Future climate projections (2022–2099) were obtained after down−scaling the data from the 29 General Circulation Models (GCMS) of Coupled Model Intercomparison Project 5 (CMIP5) and subsetted to five GCMs based on climate regimes. GCMS results were a strong indicator that climate change in this DRC dryland will result in an increase in average annual temperatures for both RCP 4.5 and 8.5, with the highest increase (3.05 °C) under hot/dry conditions for RCP8.5 and the lowest (1.04 °C) under cool/dry conditions for RCP 4.5. All the models selected for five climate regimes for 2022–2099 showed no change in the rainfall trends for RCP 4.5 (p > 0.05). The models projected yield declines of 5–25%, with less yield losses under tied ridges as an adaptation practice. The use of efficient SWC practices could therefore be a promising strategy in reducing potential losses from climate change in drylands of eastern DRC.

Multi-year Prediction of Wheat Yield under the Changing Climatic Scenarios in Central-west Terai using DSSAT Crop Model

The central-west Terai region is one of the major production domains of wheat in Nepal; however, its yield over the past three decades has been greatly affected by climate anomalies such as fluctuating temperatures, decreased solar radiation and rainfall. NASA-POWER derived weather data of Taulihawa in Kapilbastu (27.55° N, 83.667° E) district in central Terai for the past 32–33 years (1984/85 - 2017/18) were purposively selected and downloaded and validated with recorded weather data (1985/86, 1995/96, 2005/06 and 2015/16) of Department of Hydrology and Meteorology (DHM). The trend analysis for grain yield of wheat yield in Kapilvastu was drawn with the historical data of maximum and minimum temperatures and rainfall. Positive correlations between grain yields and minimum temperature and rainfall each showed an acceptable coefficient of determinations (R2). The Cropping system Model - Crop Environment Resource Synthesis (CSM-CERES)- Wheat model, embedded in Decision Support Systems for Agro-technology Transfer (DSSAT) ver 4.7 was used for multi-year predictions of wheat yields using both historically recorded and simulated climatic scenarios. Model simulated results closely agreed with the observed wheat yields recorded by the Ministry of Agriculture and Livestock Development (MoALD) in Nepal. The correlation coefficient of minimum temperature and wheat yield was 0.272 (p<0.05). The correlation between precipitation and observed and DSSAT simulated wheat yield were 0.379 (p<0.01). The multi-year predicted wheat yields using the historical weather data and by the use of the International Panel on Climate Change (IPCC, 2007) scenario embedded in DSSAT crop model showed that yield of wheat could be sustained with use of the current crop cultivars only for 2050 scenarios. Agro-climatic index, mainly temperature, was found to be more sensitive to wheat production in the Nepalese central-west Terai region. This study suggests for the development of new temperature and drought tolerant ready wheat cultivars to feed the increasingly growing Nepalese population.

Assessment of climate change on wheat (Triticum aestivum) production using crop simulation under Indo-Gangetic Plains

Climate change has very wide impact on different crops and cropping system. In north India crop like wheat also affected due to climate change. To assess the impact of climate change a study using CERES (Crop Environment Resource Synthesis) model was carried out at Kanpur, India. Three different levels of carbon dioxide (CO2) i.e. 450 ppm, 500 ppm and 550 ppm and two different levels of temperature (1̊C and 2̊C) and their interaction were used to study the impact of climate change on wheat production. The results revealed that different elevated levels of carbon dioxide concentration recorded higher production of wheat which ranges from 3–8%. Graded levels of temperatures caused drastic reduction in yield which ranges from 10–17%. Conjoint effect of temperature and carbon dioxide recorded yield losses from 1–16%.

Simulating the effect of climate change on barley yield in Ethiopia with the DSSAT‐CERES‐Barley model

AbstractClimate change is expected to have a major effect on crop production in sub‐Saharan Africa. Crop models can help to guide crop management under future climate. The objective of the study was to investigate the possible effects of climate change on Ethiopian barley (Hordeum vulgare L.) production using the Decision Support System for Agrotechnology Transfer (DSSAT)‐Crop Environment Resource Synthesis (CERES)‐Barley model. The study included field data of two barley cultivars (Traveller and EH‐1493) and four climate study areas in Ethiopia over 5 yr. Climate change scenarios were set up over 60 yr using representative concentration pathways (RCP; RCP4.5 and RCP8.5) and five global climate models (GCM). The model results indicated that the prediction of days to anthesis and maturity, as well as final grain yield, was highly accurate for cultivar Traveller with normalized RMSE (nRMSE) of 2, 1, and 12%, respectively, and for cultivar EH‐1493 with nRMSE of 2, 4, and 11%. A consistent increase in average temperature up to 5 °C and a mixed pattern of rainfall (‐61 to +86%) were projected. Yield simulations showed a potential reduction in yield up to 98% for cultivar Traveller and 63% for cultivar EH‐1493 in the future. Within a sensitivity analysis, different sowing dates, sowing densities, and fertilizer rates were tested as potential adaptation approaches to climate change. The negative effects of climate change could be mitigated by early sowing, with an increased sowing density of 25% and fertilizer rate of 50% more than what is recommended. Overall, the results indicated the ability of the CERES‐Barley model to evaluate climate change effects and adaptation options on rainfed barley production in Ethiopia.

Nutrient management may reduce global warming potential of rice cultivation in subtropical India

Agricultural practices contribute to greenhouse gas (GHG) emissions; therefore, it is essential to modify the production technologies. We analyzed decadal variation in CO2 and CH4 over a major rice cultivating area in subtropical India using GOSAT satellite data, which shows a sturdy increase. Furthermore, we carried out long-term field experiments with different nutrients management in the research farm to validate CERES–Rice (Crop Environment Resource Synthesis) and DNDC (De-nitrification and Decomposition model) models. The variations in Global warming potential per kg rice grain production over 90 years (2005–2095) are also projected. This study used a simulation technique to predict the rice yield using CERES–Rice and GWP using the DNDC model for three varied nutrient management treatments: chemical fertilizer (CF) at full (100%) recommended level (CF100), organic fertilizer using vermicompost at full recommendation (VC100), and integration of organic and chemical fertilizer (VC50 + CF50). The CF100 treatment showed the highest rate of increase in GWP as 0.014 and 0.021 kg CO2eq kg-grain season−1 in RCP 4.5 and 8.5 scenarios, respectively. Integrating organic fertilizers with chemical fertilizers may give a nearly similar yield in later decades of the century in both RCP 4.5 and 8.5 climate scenarios with substantial reductions (77% in RCP 4.5 and 66% in RCP 8.5) in the rate of change in GWP as compared to sole chemical fertilizer application. This study recommends integrated nutrient management using organic fertilizers as a feasible way to limit the GHG emission from rice fields and minimize global warming in future climate scenarios.

Simulating Potential Impacts of Future Climate Change on Post-Rainy Season Sorghum Yields in India

Given the wide use of the multi-climate model mean (MMM) for impact assessment studies, this work examines the fidelity of Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating the features of Indian summer monsoons as well as the post-rainy seasons for assessing the possible impacts of climate change on post-rainy season sorghum crop yields across India. The MMM simulations captured the spatial patterns and annual cycles of rainfall and surface air temperatures. However, bias was observed in the precipitation amounts and daily rainfall intensity. The trends in the simulations of MMM for both precipitation and temperatures were less satisfactory than the observed climate means. The Crop Environment Resource Synthesis (CERES)-sorghum model was used to estimate the potential impacts of future climate change on post-rainy season sorghum yield values. On average, post-rainy season sorghum yields are projected to vary between −4% and +40% as well as +10% and +59% in the near future (2040–2069) for RCP 4.5 and RCP 8.5, respectively, and between +20% and +70% (RCP 4.5) as well as +38% and +89% (RCP 8.5) in the far future (2070–2099). Even though surface air temperatures are increasing in future climate change projections, the findings suggest that an increase in the post-rainy season sorghum yields was due to an increase in the rainfall amounts up to 23% and an increase in the atmospheric CO2 levels by the end of the 21st century. The results suggest that the projected climate change during the post-rainy season over India is an opportunity for smallholders to capitalize on the increase in rainfall amounts and further increase sorghum yields with appropriate crop management strategies.

Modeling the effects of elevation and precipitation on Rice (Oryza sativa L.) production considering multiple planting methods and cultivars in Central China

In this study, we investigated the effects of elevation and precipitation on rice (Oryza sativa L.) production using the Crop Environment Resource Synthesis (CERES)-Rice model in Hubei province, China. We divided our study area into four zones based on elevation and precipitation. For each zone, our simulations were conducted using three planting methods: dry direct-seeded rice (DDSR), wet direct-seeded rice (WDSR), and transplanted-flooded rice (TFR), with three rice cultivars of different growth duration: Yangliangyou6 (long-duration), Huanghuazhan (mid-duration), and Lvhan1 (short-duration). Additionally, the optimal irrigation strategy for WDSR was determined with the CERES-Rice model. Our results indicated that the yields of WDSR with the optimal irrigation strategy were comparable with those of TFR in low-elevation regions but were less than the TFR yields in high-elevation areas. Furthermore, the rice yields increased at first and then decreased with increasing elevation, which was affected by growing period length and photosynthesis rate. Compared with the other two cultivars, the short-duration cultivar may be more suitable for growing in high-elevation regions. In addition, high precipitation could facilitate the cultivation of the long-duration cultivar in low-elevation regions, as it gives DDSR a yield potential comparable to that of WDSR for the short-duration cultivar in high-elevation regions. This study could help farmers choose optimal field management practices based on elevation and precipitation, ensuring sustainable and improved rice production.

Climate-associated major food crops production change under multi-scenario in China

To inform targeted adaptation measures, comprehensive assessments of climate change impacts on agricultural systems are urgently needed. The current study analyzed the production (including phenology, yield, ET, and WUE) of major crops in the near future (2011–2040) through probabilistic assessment. The Crop-Environment Resource Synthesis (CERES)-Wheat/Maize model was driven by ensemble climate projections from five global climate models (GCMs) under three emission scenarios (RCP2.6, RCP4.5, RCP8.5). Results showed that: (1) Compared with the base period, the probability of advanced maturity for wheat and maize was 90.36–91.18% and 62.96–64.50%, respectively. The probability of yield reduction for wheat and maize was 64.12–68.93% and 40.44–41.41%, respectively. The probability of water use efficiency (WUE) reduction for wheat and maize was 51.09–53.94% and 35.86–37.93%, respectively. (2) In the absence of adaptation measures, substantial yield loss was found in major crop-producing areas, including the northern winter wheat planting area and Huang-Huai Plain spring-summer maize zone. The spatial overlap of the vulnerable area will exacerbate food insecurity. (3) The decrease in wheat yield and WUE were both greater than that of maize. Replacing highly sensitive crops with heat-tolerant varieties and dietary diversity should be advocated to cope with future climate change. The results will contribute to adaptive decision-making in China.

Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China

Timely and accurate regional crop-yield estimates are crucial for guiding agronomic practices and policies to improve food security. In this study, a crop-growth model was integrated with time series of remotely sensed data through deep learning (DL) methods to improve the accuracy of regional wheat-yield estimations in Henan Province, China. Firstly, the time series of moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) were input into the long short-term memory network (LSTM) model to identify the wheat-growing region, which was further used to estimate wheat areas at the municipal and county levels. Then, the leaf area index (LAI) and grain-yield time series simulated by the Crop Environment REsource Synthesis for Wheat (CERES-Wheat) model were used to train and evaluate the LSTM, one-dimensional convolutional neural network (1-D CNN) and random forest (RF) models, respectively. Finally, an exponential model of the relationship between the field-measured LAI and MODIS NDVI was applied to obtain the regional LAI, which was input into the trained LSTM, 1-D CNN and RF models to estimate wheat yields within the wheat-growing region. The results showed that the linear correlations between the estimated wheat areas and the statistical areas were significant at both the municipal and county levels. The LSTM model provided more accurate estimates of wheat yields, with higher R2 values and lower root mean square error (RMSE) and mean relative error (MRE) values than the 1-D CNN and RF models. The LSTM model has an inherent advantage in capturing phenological information contained in the time series of the MODIS-derived LAI, which is important for satellite-based crop-yield estimates.

Improving CERES-Wheat Yield Forecasts by Assimilating Dynamic Landsat-Based Leaf Area Index: A Case Study in Iran

In this study, we tried to address the applicability of using dynamic remotely sensed data into a static crop model to capture the yield spatiotemporal variability at the field scale. Taking the example of the crop environment resource synthesis for wheat (CERES-wheat), the model was calibrated, improved, and validated using three years of winter wheat field measurement data (growing seasons of 2017–2019). We assimilated the Landsat-based leaf area index (LAI) into the model using the particle filter approach. Four vegetation indices, including NDVI, SAVI, EVI, and EVI-2, were evaluated to identify winter wheat LAI’s best estimator. A linear regression of Landsat-EVI-2 was found to be the most accurate representation of LAI (LAI = 10.08 × EVI-2 − 0.53) with R2 = 0.87, and mean bias error = − 2.04. The higher LAI accuracy from EVI-2 was attributed to the soil and canopy background noise reduction and accounting for certain atmospheric conditions. Assimilating the LAI based on Landsat-EVI-2 into the CERES model improved the model’s overall performance, particularly for grain yield and biomass simulations. The default model predicted LAImax, grain yield, and biomass at 5.1 cm2 cm−2, 8.3 Mg ha−1, and 14.9 Mg ha−1 with RMSE of 1.44, 0.91 Mg ha−1, and 1.2 Mg ha−1, respectively, while the modified model (using the Landsat-EVI-2 data) predicated these values at 6.6 cm2 cm−2, 9.9 Mg ha−1, and 16.6 Mg ha−1 with RMSE of 0.81, 0.54 Mg ha−1, and 0.62 Mg ha−1, respectively.

Effects of temperature frequency trends on projected japonica rice (Oryza sativa L.) yield and dry matter distribution with elevated carbon dioxide.

In this study, we investigated the effects of temperature frequency trends on the projected yield and dry matter distribution of japonica rice (Oryza sativa L.) with elevated carbon dioxide (CO2) under future climate change scenarios in northwestern China. The Crop Environment Resource Synthesis (CERES)-Rice model was forced with the outputs from three general circulation models (GCMs) to project the rice growth and yield. Future temperature trends had the most significant impact on rice growth, and the frequency of higher than optimal temperatures (∼24–28 oC) for rice growth showed a marked increase in the future, which greatly restricted photosynthesis. The frequency of extreme temperatures (>35 oC) also increased, exerting a strong impact on rice fertilization and producing a significantly reduced yield. Although the increased temperature suppressed photosynthetic production, the elevated CO2 stimulated this production; therefore, the net result was determined by the dominant process. The aboveground biomass at harvest trended downward when temperature became the major factor in photosynthetic production and trended upward when CO2-fertilization dominated the process. The trends for the leaf and stem dry matter at harvest were affected not only by changes in photosynthesis but also by the dry matter distribution to the panicles. The trends for the rice panicle dry matter at harvest were closely related to the effects of temperature and CO2 on photosynthetic production, and extreme temperatures also remarkably affected these trends by reducing the number of fertilized spikelets. The trends of rice yield were very similar to those of panicle dry matter because the panicle dry matter is mostly composed of grain weight (yield). This study provides a better understanding of the japonica rice processes, particularly under extreme climate scenarios, which will likely become more frequent in the future.

Simulation of dryland maize growth and evapotranspiration using DSSAT‐CERES‐Maize model

AbstractEvapotranspiration (ET) is a crucial component of plant water use and ecosystem energy balance. This study aims to simulate maize (Zea mays L.) ET using a crop modeling system and compare that to ET measured using an eddy‐covariance (EC) system. An EC system was used to collect growing season ET data from a rainfed maize field in 2017, 2018, and 2019. Supporting soil and plant growth data were also collected. The Decision Support System for Agrotechnology Transfer (DSSAT)‐Crop Environment Resource Synthesis (CERES)‐Maize model was used in this study. Results from DSSAT's ET simulation methods, FAO‐56 and Priestley–Taylor, were compared to measured ET. Results indicated a low coefficient of determination (R2) between simulated and measured ET, due to overestimation of simulated ET in all 3 yr. The average percent root mean square error for the daily ET simulation across all 3 yr was 34.6% for the Priestley–Taylor method and 30.4% for the FAO‐56 method. For the FAO‐56 simulation, the average seasonal overestimation compared to measured ET was 59 mm and the average R2 was .59. For the Priestley–Taylor method, the average seasonal overestimation was 72 mm and the average R2 was .57. Possible sources of error contributing to this overestimation include DSSAT's potential ET estimations, crop coefficient estimation, and the simulation of soil water balance in vertisols with high swell‐shrink clay minerals. The mixed simulation results, particularly compared to those seen in studies of irrigated crops, indicate a greater need to improve ET estimation capabilities in rainfed systems.