Changes In Rice Yield Research Articles

Game analysis of future rice yield changes in China based on explainable machine-learning and planting date optimization

ContextGlobal warming's escalating severity necessitates sophisticated approaches for predicting rice yield. Research QuestionCombining crop models with data-driven techniques, such as machine learning, can more effectively grasp the complex interplay of variables influencing crop growth. It remains a significant challenge to balance accuracy and interpretability in such hybrid models. MethodsThe research integrated the Decision Support System for Agrotechnology Transfer (DSSAT) with statistical and machine learning models respectively, to assess rice yield changes in China under four future Shared Socio-economic Pathway (SSP). SSPs are scenarios that integrate socioeconomic trends with greenhouse gas emissions and radiative forcing pathways, which affect the phenology and yield of rice. The Shapley Additive Explanation (SHAP) method was employed to interpret the model, effectively determining the interplay among variables influenced rice yields. Mitigated the negative impacts of climate change on rice yield through the planting date optimization. ResultsProjections indicate significant rice yield losses in China without CO2, worsening with increased radiative forcing (p < 0.001). Considering rising CO2, single-season rice yields are projected to increase by 0.1–3.6 %, early rice by 4.6–9.5 %, while late rice yields are still decrease by 2.3–8.8 %. The rising CO2 can offset yield losses for single and early rice but not for late rice. The hybrid approach which combined the Random Forest (RF) with the DSSAT performed best in predicting rice yield. Studies showed that rising temperatures caused rice yield losses in China, yet we found that Growing Degree Days (GDD) exerted a more negative impact (p < 0.001). In high-precipitation regions, deep soil moisture is more influential than shallow soil moisture, whereas the reverse was true in drier areas (p < 0.001). Advancing planting dates for early and single rice and delaying for late rice can increase yields (p < 0.001). Adjusting to optimal planting dates, single-season rice yields increased by 3.3–6.3 %, early rice increased by 9.7–18.3 %, while late rice still decreased by 1.0–4.7 %. ConclusionsWithout considering the impact of CO2, significant rice yield losses in China are projected. Even with the fertilization effect of CO2, rice yields remain negatively impacted by climate change. However, implementing appropriate measures, such as optimizing planting dates, can help Chinese rice production benefit under changing climate. ImplicationsThis study offers insights into balancing accuracy and interpretability in hybrid models and provides guidance for local policymakers to address future climate change.

Assessment of Different Frameworks for Addressing Climate Change Impact on Crop Production and Water Requirement

Various methodologies are used to estimate the impact of changing climatic factors, such as precipitation, temperature, and solar radiation, on crop production and water demand. In this study, the changes in rice yield, water demand, and crop phenology were estimated with varying CO2 concentration and an ensemble of general circulation models (GCMs), using a decision support system for agrotechnology transfer (DSSAT), a crop growth model. The measured CO2 concentration of 400 ppm from the Keeling curve, was used as the default CO2 concentration to estimate yield, water demand, and phenology. These outputs, obtained with the default concentration, were compared with the results from climate change scenarios’ concentrations. Further, the outputs corresponding to the ensembled GCMs’ climate data were obtained, and the results were compared with the ensembled crop model outputs simulated with each GCM. The yield was found to increase with the increase in CO2 concentration up to a certain threshold, whereas water demand and phenology were observed to decrease with the increase in CO2 concentration. The two approaches of the ensemble technique to obtain final outputs from DSSAT results did not show a large difference in the predictions.

Luxury application of biochar does not enhance rice yield and methane mitigation: a review and data analysis

PurposeIt is unclear whether a higher biochar (BC) application rate enhances rice (Oryza sativa L.) yield and reduces CH4 emissions. This study investigated changes in rice yield and CH4 emissions with varying BC application rates.MethodsData on rice yield and CH4 emission from paddies amended with or without BC were collected from the literature, and the biochar effects were analyzed using the data set.ResultsAcross the biochar application rate from 2 to 48 t ha-1, the rice yield increased (by 10.8%) while the area-scaled (by 14.4%) and yield-scaled CH4 emission (by 22.2%) decreased. However, the correlation of BC application rates with rice yield and CH4 mitigation was not significant, implying that a higher BC application rate did not enhance rice yield and CH4 reduction. Interestingly, for a data set showing increased rice yield and decreased CH4 emission by BC, the magnitude of change in the rice yield and CH4 mitigation per unit weight of BC (1 t ha-1) decreased with an increase in the BC application rate. These results suggest that BC effects on rice yield and CH4 mitigation are not additive, probably because of the decreases in the inherent capacity of unit weight of BC to enhance rice yield and reduce CH4 emission, which might be caused by the adverse effects of toxic compounds contained in BC, losses of BC, and a higher degree of nutrient immobilization by BC.ConclusionsAnnual BC application at a low rate (e.g., 2 t ha-1) rather than a luxury application may be an effective and economical strategy for long-term rice yield enhancement and CH4 mitigation using BC.

Temporal variation of the relationships between rice yield and climate variables since 1925.

Long-term time-series datasets of crop yield and climate variables are necessary to study the temporal variation of climate effects on crops. The aim of this study was to broadly assess assessment of the effects of climate on rice, and the associated temporal variations of the effects during the long-term period. We conducted field experiments in Taiwan from 1925 to 2019 to collect and analyze rice yield data and evaluate the impacts of changes in average temperature, diurnal temperature range (DTR), rainfall, and sunshine duration on rice yield during cool and warm cropping seasons. We then estimated the relationships between annual grain yield and the climate variables using the time series of their first difference values. We also computed the total relative and annual actual yield changes using regression coefficients for each climate variable for the intervals 1925-1944, 1945-1983, and 1996-2019 to reveal the impacts of climate change on yields and the associated temporal variations during the overall experimental period. The annual daily average temperature calculated from the trend of the regression lines increased by 0.94-1.03 °C during the 95-year period. The maximum temperature remained steady while the minimum temperature increased, leading to decreased DTR. The total annual rainfall decreased by 237-352 mm and the annual total sunshine duration decreased by 93.9-238.9 h during the experimental period. We observed that during the cool cropping season, yield response to temperature change decreased, while that to DTR and rainfall changes increased. During the warm cropping season, all the yield responses to temperature, DTR, and rainfall changes were negative throughout the experimental period. In recent years (1996-2019) the estimated annual actual rice yield changes during the cool cropping season were negatively affected by climate variables (except for sunshine duration), and slightly positively affected (except for temperature) during the warm cropping season. Compared to the effects of temperature and DTR, those of rainfall and sunshine duration on rice yield changes were weak. This study contributes to provide impacts of climate change on rice yield and associated long-term temporal variations over nearly a century.

Regional inequalities of future climate change impact on rice (Oryza sativa L.) yield in China

The implications of climate change for rice yield have significant repercussions for food security, particularly in China, where rice cultivation is diverse, involving various cropping intensities, management practices, and climate conditions across numerous regions. The regional discrepancies in the impact of climate change on rice yield in China, however, are yet to be fully understood. Using the ORYZA(v3) model and future climate data from 2025 to 2084, gathered from ten climate models and three climate change scenarios (RCP2.6, RCP4.5, and RCP8.5), we conducted an investigation into these regional discrepancies. Our findings suggest a projected average decline in rice yield ranging from 3.7 % to 16.4 % under both rainfed and fully irrigated conditions across different scenarios. Central, eastern, and northwestern China could face the most significant climate change impacts on both rainfed and irrigated rice, with yield reductions reaching 41.5 %. In contrast, low levels of climate change under the RCP2.6 scenario may benefit northeastern (2.4 %) and southern (1.0 %) regions for rainfed and irrigated rice, respectively. Fertilization effects from elevated CO2 could counterbalance climate change's negative impact, resulting in yield increases in all Chinese rice-growing regions, excluding the northwest. The primary factor influencing rice yield changes in all regions under the RCP4.5 and RCP8.5 scenarios was temperature. However, precipitation, solar radiation, and relative humidity had notable and sometimes dominant effects, especially under the RCP2.6 scenario. These results highlight the divergent, even contradictory, rice yield responses to climate change across China, underlining the need to account for regional differences in large-scale impact studies. The study's findings can inform future policy decisions regarding ensuring regional and national food security in China.

Dynamic diffusion of hybrid rice varieties and the effect on rice production: evidence from China

The widespread adoption of hybrid rice varieties in China is a successful example, showing the role of agricultural technology in terms of food security. However, the dynamic diffusion of hybrid rice varieties and their effect on rice production requires further study. Based on data on hybrid rice adoption at the provincial level from 1984 to 2011, we applied the Ordinary Least Squares (OLS) and Geographically and Temporally Weighted Regression (GTWR) models to investigate the spatial and temporal effects of hybrid rice adoption at national and provincial levels. Overall, the effects of hybrid rice adoption on rice production have decreased over time. However, the results showed possible spillover and crowding effects of hybrid rice adoption across provinces. In particular, the development of hybrid rice varieties in Hunan province has had a significant influence on changes in rice yield and the distribution of rice areas in other regions. This study, therefore, serves as a reference in understanding the dynamic distribution of high-yield rice variety adoption in relation to food security and for designing appropriate agricultural extension strategies. However, further research is needed to identify the determinants affecting changes in rice farming in complex environments and associated ecological systems.

GF14f gene is negatively associated with yield and grain chalkiness under rice ratooning.

Ratoon rice cropping has been shown to provide new insights into overcoming the current challenges of rice production in southern China. However, the potential mechanisms impacting yield and grain quality under rice ratooning remain unclear. In this study, changes in yield performance and distinct improvements in grain chalkiness in ratoon rice were thoroughly investigated, using physiological, molecular and transcriptomic analysis. Rice ratooning induced an extensive carbon reserve remobilization in combination with an impact on grain filling, starch biosynthesis, and ultimately, an optimization in starch composition and structure in the endosperm. Furthermore, these variations were shown to be associated with a protein-coding gene: GF14f (encoding GF14f isoform of 14-3-3 proteins) and such gene negatively impacts oxidative and environmental resistance in ratoon rice. Our findings suggested that this genetic regulation by GF14f gene was the main cause leading to changes in rice yield and grain chalkiness improvement of ratoon rice, irrespective of seasonal or environmental effects. A further significance was to see how yield performance and grain quality of ratoon rice were able to be achieved at higher levels via suppression of GF14f.

Agronomic Improvements, Not Climate, Underpin Recent Rice Yield Gains in Changing Environments

Food security depends not only on the extent of climate change but also on the compensatory potentials of agronomic improvements. However, the separate contribution of these agronomic factors to rice yield remains largely unknown. Here we distinguished the impacts and relative contributions on rice yield based on statistical models and machine learning by using an observation database collected from 52 agro-meteorological stations in China from 1981 to 2018. Agronomic improvements are responsible for more than 40% of the observed rice yield change, ranging from 42.9% to 96.5% in different cropping types, and the effect increased with the latitude. Among the management considered, sowing date adjustment contributes most to late and early rice yield. Response of rice yield to nighttime temperature was stronger than that to daytime temperature, and wind speed is the main climatic contributing factor to early rice yield. The effects of wind speed on rice yield should be considered for the adaptation measures. This observation-based evidence may help guide agricultural priorities in mitigating the impact of climate change on rice yield.

Studying the changes in rice yield and water balance in Guilan Province affected by climate change

سابقه و هدف: افزایش گازهای گلخانه‌ای، تأثیرهای متفاوتی بر عملکرد گیاهان زراعی خواهد داشت، به ­طوریکه برهمکنش این اثرها ممکن است موجب افزایش یا کاهش محصول شود. مدل‌های شبیه‌سازی گیاهان زراعی برای بررسی سطح ­های مختلف مدیریت زراعی و محیطی استفاده ‌شده است این مطالعه با هدف بررسی مدل AquaCrop براساس اقلیم گذشته، حال و آینده در شهر رشت واقع در استان گیلان جهت دستیابی به حداکثر بهره وری آب و عملکرد دانه برنج صورت گرفت.مواد و روش ­ها: به‌منظور بررسی میزان تغییرات عملکرد برنج، موازنه و بهره ­وری آب در شهر رشت واقع در استان گیلان تحت اقلیم گذشته، کنونی و آینده از مدل AquaCrop استفاده شد. بدین منظور از داده ­های بلندمدت (بالای 30 سال) جهت بررسی وضعیت عملکرد و موازنه آب در کشت برنج در اقلیم گذشته و کنونی استفاده شد. همچنین با استفاده از نرم ­افزار LARS-WG6 داده ­های هواشناسی 83 سال آینده براساس داده‌های روزانه هواشناسی موجود تولید شد. بررسی مدل AquaCrop در اقلیم گذشته، حال و آینده براساس داده ­های روزانه دمای کمینه، بیشینه، بارش و ساعت آفتابی صورت گرفت. تیمارهای مورد بررسی شامل چهار سطح آبیاری شامل تیمار 55، 70، 85 و 100 درصد نیاز آبی و تیمارهای تاریخ کاشت یک اردیبهشت،‌ 20 اردیبهشت و 10 خرداد بود. با بررسی اثر سطح­ های تیماری مختلف براساس سناریوهای تغییر اقلیم RCP 4.5 و RCP 8.5، میزان تغییرات عملکرد دانه، تبخیر - تعرق و بهره ­وری آب مبتنی بر تبخیر و تعرق در اقلیم گذشته، کنونی و آینده مورد بررسی قرار گرفت. همچنین بهترین تیمار آبیاری و تاریخ کاشت جهت افزایش عملکرد برنج و کاهش میزان مصرف آب معرفی شد.نتایج و بحث: نتایج ارزیابی نشان داد که مدل LARS-WG6 با دقت بالایی قادر به شبیه‌سازی مؤلفه­ های اقلیمی دما، بارش و تابش می­ باشد. نتایج نشان داد که دمای کمینه و بیشینه طی سناریوهای تغییر اقلیم روند افزایشی و میزان تابش و بارش روند کاهشی داشته است. بررسی عملکرد زیستی و دانه برنج تحت RCP 4.5 و RCP 8.5 نشان داد که بیشترین عملکرد دانه و زیست ­توده در آبیاری 100 درصد نیاز آبی و تاریخ کاشت یکم اردیبهشت به ­دست آمد. بررسی بهره ­وری آب نشان داد که تیمار آبیاری 100 درصد نیاز آبی و تاریخ کاشت 10 خرداد نقش مؤثری در افزایش ذخیره آب خاک و کاهش تبخیر و تعرق از سطح خاک داشته است. براساس مقادیر تبخیر-تعرق به ­دست آمده تحت سناریوهای مورد بررسی، بیشترین بهره ­وری تولید دانه مبتنی بر تبخیر - تعرق در آبیاری 100 درصد نیاز آبی و تاریخ کاشت 10 خرداد به ­دست‌ آمد.نتیجه ­گیری: با توجه به نتایج به­ دست آمده، با در نظر گرفتن بهره‌وری مصرف آب و میزان عملکرد، با توجه به مشکل­ هایی که در آینده از جمله کمبود آب وجود خواهد داشت، به نظر می‌رسد کشت دیرهنگام برنج در شرایط کمبود آب راهکار مناسبی باشد، اما در شرایطی که در محیط کمبود آب وجود نداشته باشد، کشت زودهنگام برنج مانند یکم اردیبهشت می‌تواند سبب افزایش تولید شود. بررسی سطح­ های آبیاری نشان داد که تولید دانه مؤثرترین عامل در افزایش بهره‌وری مصرف آب است و استفاده از سطح­ های کم­ آبیاری نقش مؤثری در افزایش بهره‌وری آب نخواهد داشت.

Nitrogen-Reduction in Intensive Cultivation Improved Nitrogen Fertilizer Utilization Efficiency and Soil Nitrogen Mineralization of Double-Cropped Rice

Under the current rice cropping system, excessive nitrogen application has become a major issue that needs to be changed, and nitrogen reduction has become a hot research topic in recent years. The use of optimum planting density is becoming a common agronomic management system in addition to nitrogen reduction, especially under double cropping rice systems. In this paper, changes in rice yield, nitrogen-use efficiency (NUE) and net N mineralization under dense planting with a reduced nitrogen rate (DPRN) were studied. By comparing DPRN with high-nitrogen sparse planting (SPHN), we found that the population tiller number (tiller number per unit area) increased by 9–27% under DPRN cultivation. Nitrogen accumulation under DPRN treatment of double-cropped rice was basically stable. NUE under DPRN was significantly higher by 1.3–22.7% compared to SPHN. The partial factor productivity of applied N (PFPN) was significantly higher than that of SPHN, with an increase of 4.3–22.8%. The net N mineralized of double-cropped rice under DPRN increased at different stages, and the increase in late-season rice (LSR) was greater than that of early-season rice (ESR). The highest net N mineralized in double cropping rice at different stages was found in the dense planting treatment (DP) and N2 (120 kg N h−1). In conclusion, DPRN cultivation of double-cropped rice could be accepted as a proper management strategy for reducing nitrogen input, improving NUE and promoting soil nitrogen mineralization under given conditions.

Evaluation of the changes in rice yield and soil chemical properties and estimation of potassium supply mechanism from a paddy-field soil under a 95-year long-term fertilizer experiment in Aichi Prefecture, Japan

Evaluation of the changes in rice yield and soil chemical properties and estimation of potassium supply mechanism from a paddy-field soil under a 95-year long-term fertilizer experiment in Aichi Prefecture, Japan

Rice yields and the effect of weed management in an organic production system with winter flooding

ABSTRACT Winter flooding of paddy rice production is characterized by flooding of paddy fields during the winter fallow season. It has attracted attention in terms of its ecological function, supporting the biodiversity of paddy field ecosystems. Therefore, recent winter flooding in Japan has been conducted by organic farming. However, it is not well documented how organic farming with winter flooding affect the productivity of rice. We conducted field experiments to examine changes in rice yield in organic farming with winter flooding compared with those in conventional and organic farming over the period of 5 and 9 years. The yields in organic farming with winter flooding were lower than those in conventional farming but were almost the same as those in organic farming. We found a trend that shows the relative yield of organic farming with winter flooding compared to that of conventional farming increased during the first several years after the conversion from conventional farming to organic farming with winter flooding, but that it decreased after several years. This trend may be caused by the flourishing of weeds during continuous organic farming with winter flooding. Therefore, we tested four weed control methods: rice bran application, mechanical weeding, repeated puddling, and side-row fertilizer application. Although each method suppressed weeds to certain extents, some combinations of different weeding methods more effectively suppressed weed growth, thereby resulting in enhanced rice yields. We found a trend in rice yield and a possibility of combining several weed control methods in organic farming with winter flooding.

Artificial intelligence approach to estimating rice yield*

AbstractAfter wheat, rice is one of the most important agricultural products in the world, and Iran has a special position here with annual production of more than 2 million t of rice. Evaluation of crop yield has an important role in agricultural policy making due to different conditions and restrictions. Estimating rice yield is a key factor in food security. Any change in the effective parameters can cause changes in rice yield and therefore the food security of the population will be affected. In this study, rice crop yield was estimated by artificial neural networks (ANNs) and ANN‐genetic programming (GP) in 2011 and 2015. Rainfall, permeability, soil texture, land type, evapotranspiration and inlet and inflow and outflow water to paddy lands were used as inputs. The results showed that the ANN‐GP with a root mean square error (RMSE = 80.8 kg ha‾¹) and a correlation coefficient (CC = 0.91) was more accurate than the stand‐alone ANN (with RMSE = 139 kg ha‾¹ and CC = 0.67). Finally, the effect of each input parameter on rice yield was evaluated. Irrigation, drainage and soil type parameters had the best impact rank, with 36, 28 and 31%, respectively. Therefore, the proposed method can act as an efficient tool in estimating rice yield and help decision makers to manage and develop the agricultural system.

Future changes in rice yield over Kerala using climate change scenario from high resolution global climate model projection

The impact of climate change on agricultural yield is one amongst the major concerns the world is witnessing. Our study focusses on rice yield prediction for an agricultural research station in Kerala with the help of climate change scenario input from the Meteorological Research Institute (MRI) Global Climate Model (GCM) projection under Representative Concentration Pathway 8.5 (RCP8.5). We have used Cropping System Model (CSM) Crop Estimation through Resource and Environment Synthesis (CERES) Rice within Decision Support System for Agrotechnology Transfer (DSSAT) package for predicting the yield. Our study has the novelty of using very high-resolution climate data from a model which is highly skilful in capturing the present-day climate features and climatic trends over India (in particular, over the Western Ghats), as input for simulating the future crop yield. From this study, we find that the rice yield decreases due to rise in temperature and reduction in rainfall, thereby reducing the crops maturity time in the future. Based on our results, the adaptation measures suggested to achieve better yield under future warming conditions are: (i) to opt for alternative rice varieties which have tolerance to high temperatures and consume less water, and (ii) shifting of planting date to the most appropriate window.

Modelling rice yield with temperature optima of rice productivity derived from satellite NIRv in tropical monsoon area

Tropical rice production is at risk from rising temperature. Understanding regional and seasonal heterogeneity of optimum temperatures for rice production is important for model simulation to predict rice yield change under climate change. However, studies or tools for widely observation of crop responses to temperature over broad spatial scales with long time spans are limited. In this study, we detected optimum temperature range for rice gross primary production (ToptGPP) in the lower Gangetic plains and delta region using the near-infrared reflectance of vegetation (NIRV), which is a new photosynthetic proxy, to improve ORYZA model performance in high-temperature season and assessed how tropical rice would respond to temperature increase in the study area. According to satellite observations of NIRV from 2001 to 2015, current ambient air temperature has exceeded the mean ToptGPP of Boro rice (24.8 ± 1.8 °C) and Aman rice (26.7 ± 1.2 °C) in the lower Gangetic plains and delta region, suggesting a downtrend of rice production under future warming. The detection results show that rice has lower ToptGPP in the regions with more drought stress and lower background temperature under water-limited conditions. Furthermore, the model modified by NIRv-ToptGPP shows better performance in potential yields, especially in high-temperature seasons on the region scale. Without CO2 fertilization effect, each degree-Celsius increase is expected to reduce rice potential yields by 4.9 ± 1.6% based on the default ToptGPP range in ORYZA model and by 7.0 ± 1.2% based on the detected NIRv-ToptGPP range in the study area. This study implies that global grid-based model simulation may underestimate sensitivity of tropical rice yield to temperature rise due to the neglect of regional and seasonal heterogeneity of ToptGPP. NIRV makes it possible to determine local optimal temperatures for crop production, and to improve grid-based modelling across various agricultural systems in different growing seasons at the regional scale.

Changes in rice yield and yield stability in China during the past six decades.

Increasing rice yield and its stability are important to achieving the sustainability of rice production. Rice yields have increased substantially in China during recent decades, but information on the trend in yield stability has been limited. In this study, the trends in rice yield and rice yield stability from 1949 to 2015 were analysed in China's rice production. The results showed that rice yields for all 16 provinces presented an increasing trend during the study period. The national annual rice yield gain was 86.0 kg ha-1 during the last six decades, varying from 48.4 to 106.0 kg ha-1 in different provinces and exhibiting linear, bilinear, or trilinear relationships. Remarkably, the annual yield increase was smaller in provinces dominated by double rice cropping systems than in the other provinces. Notably, rice yield stagnations have occurred in recent years at provincial and national levels. Absolute residuals over time showed slight increases in four of 16 provinces, indicating a reduction in yield stability in these four provinces. However, the relative yield residuals exhibited a clear trend towards increased yield stability for all 16 provinces. The rice yields of newer cultivars planted with improved crop management practices were consistently higher and more stable than with cultivars from the beginning of the study period. This study revealed that rice yield in China has increased substantially, accompanied by improved stability over the last six decades. Given the spatial difference, this study emphasizes the priority of orienting long-term on-farm studies to investigate yield stability. © 2020 Society of Chemical Industry.

Assessment of climate change impact on rice yield and water footprint of large-scale and individual farming in Thailand

Large-scale farming (participation in large-scale agricultural extension program) and individual farming (no participation) are two farming management practices of rice cultivation in Thailand, both of which cause significant water consumption and degradation and are vulnerable to climate change. However, given that climate change will influence both grain yield and water resource availability, it is not fully understood which type of farming management practice is more adaptive to climate change. This study aims to evaluate the adaptation capabilities of large-scale and individual farming by simulating rice yield changes under future climatic conditions and estimating the climate change impact on the water footprint (WF) of rice production. Rice management practices were obtained from large-scale and individual farming. Five General Circulation Models of RCP4.5 and RCP8.5 scenarios under four future time periods were used as future climate projections. Simulation results show a remarkable increase in rice yield of individual and large-scale farming under RCP4.5, ranging from 1.3 to 29.8% and 2.0 to 30.8%, respectively, whereas it fluctuates from 11.7 to −29.0% and 8.3 to −20.8% under RCP8.5 for individual and large-scale farming, respectively. The projected total WF of rice production under RCP4.5 will decline, ranging from −10.0 to −43.0% and −0.5 to −67.0% for individual and large-scale farming, respectively. Conversely, the RCP8.5 shows a fluctuation in projected total WF of −26.5 to 63.3% and −51.1 to 60.0% for individual and large-scale farming, respectively. The total WF, mainly grey WF, in large-scale farming is lower than in individual farming. The increase of rice yield under RCP4.5 is due to an increment of temperature and precipitation, resulting in a decrease of the total WF and vice versa for RCP8.5. The large-scale farms are highlighted as adopting appropriate management practices for rice production in which they can maintain rice yield and reduce grey WF.

DEEP LEARNING-BASED ANALYSIS OF THE RELATIONSHIPS BETWEEN CLIMATE CHANGE AND CROP YIELD IN CHINA

Abstract. Climate change is an important factor in vegetation growth, and it is very significant to understand the relationship between climate change and rice yield. China is a food-importing country whose grain consumption is higher than grain production, and which relies on imports of rice, soybean, wheat and other grains. Therefore, in order to secure food security for 1.6 billion people in China, it is necessary to grasp the relationship between climate change and rice yield. In this study, 16 administrative districts in China were selected and designated as study area. This study used annual rice production from the USDA (United States Department of Agriculture) for each of China’s major administrative regions from 1979 to 2009, as well as average climate data from July to August, which were meteorological observations collected from the CRU (Climate Research Unit). Using this data, the rice crop was increased in 10 administrative regions in China and the reduction in rice harvest in 6 administrative areas was confirmed. The relationship between selected rice production and climate change was nonlinear and modelled using a deep neural network, and the validation statistics showed that the performance of DNN was 32-33% better than that of MLR (multiple linear regression). Therefore, a more quantitative analysis of the relationship between climate change and rice yield changes has been made possible through our prediction model. This study is expected to contribute to better food self-sufficiency in China and forecast future grain yields.

Impacts of Sulfate Geoengineering on Rice Yield in China: Results From a Multimodel Ensemble

AbstractSulfate geoengineering could mitigate global warming via injecting SO2 into the stratosphere. However, its impacts on regional climate might lead to adverse consequences for local agriculture. In this study, we simulated the impacts of sulfate geoengineering on rice yield in China both with sufficient irrigation (irrigated) and without irrigation (rainfed). We used Geoengineering Model Intercomparison Project G4 climates from six climate models to force the ORYZA version 3 crop model to simulate rice yields under sulfate geoengineering scenario. G4 prescribes a sulfate injection to offset the radiative forcing in the Representative Concentration Pathway (RCP) 4.5 scenario. Results indicated that during the last 15 years of G4 sulfate geoengineering (i.e., 2055–2069), the implementation of sulfate geoengineering increases rice yields in most areas in China comparing with that without the implementation of sulfate geoengineering (i.e., RCP4.5), with a yield increase of 5.3 ± 5.7% for irrigated yield and 4.8 ± 7.3% for rainfed yield. After the termination of sulfate geoengineering, the irrigated rice yield in G4 is still significantly higher than that in RCP4.5, but such increase is not significant for rainfed yield. Temperature is always the dominant factor that drives the rice yield change no matter under irrigated or rainfed conditions, but the effect from solar radiation cannot be ignored in southern China.

Climate-associated rice yield change in the Northeast China Plain: A simulation analysis based on CMIP5 multi-model ensemble projection

Multi-model ensemble climate projections in combination with crop models are increasingly used to assess the impact of future climate change on agricultural systems. In this study, we used a biophysical process-oriented CERES-Rice crop model driven by downscaled future climate data from 28 Global Climate Models (GCMs) under two emissions scenarios: representative concentration pathway (RCP) 4.5 and RCP8.5, for phase five of the Coupled Model Intercomparison Project (CMIP5) to project the effects of climate change on rice yields in three future time periods in the Northeast China Plain (NECP). The results showed that without consideration of CO2 effects, rice yield would increase by 1.3%, 1.3%, and 0.4% in the 2030s, 2060s, and 2090s, respectively, under the RCP4.5 scenario. Rice yield would change by +1.1%, −2.3%, and −10.7% in the 2030s, 2060s, and 2090s, respectively, under the RCP8.5 scenario. With consideration of CO2 effects, rice yield during the 2030s, 2060s, and 2090s would increase by 5.4%, 10.0%, and 11.6% under RCP4.5, and by 6.4%, 12.9%, and 15.6% under RCP8.5, respectively. The rice-growing season would be shortened by 2 to 5 weeks in the future. Overall, the future climate would have positive effects on rice yields in the NECP. Although uncertainties in our study on the impact of climate change on rice might arise from the choice of crop model and GCMs, the results are important for informing policy makers and developing appropriate strategies to improve rice productivity in China.