Inter-annual Yield Variability Research Articles

Global de-trending significantly improves the accuracy of XGBoost-based county-level maize and soybean yield prediction in the Midwestern United States

ABSTRACT The application of machine learning in crop yield prediction has gained considerable traction, yet uncertainties persist regarding the impact of the yield trends on these predictions and the differences between the detrending methods. In our study, we utilized extreme gradient boosting (XGBoost) to scrutinize the effects of no trend processing (NTP), input year as a feature (IYF), input average yield as a feature (IAYF), input linear yield as a feature (ILYF), and the global detrending method (GDT) on the yield prediction of maize and soybean in the Midwestern United States. Based on our findings, compared with that of NTP, the incorporation of the yield trend as a predictor in XGBoost significantly improved the accuracy and reduced the uncertainty of the yield prediction. Notably, GDT emerged as a standout performer, significantly reducing the average yield prediction error by 0.091 t/ha for soybean and 0.158 t/ha for maize with respect to NTP, and concurrently improving the determination coefficient (R2) by 20.6% and 19.6% for soybean and maize, respectively. Compared with IYF, IAYF, and ILYF, GDT showed substantial improvements ranging from 3.8% to 12.7% in R2 for soybean and 3.6% to 12.7% for maize. The SHapley Additive ExPlanations (SHAP) framework showed that the enhanced vegetation index (EVI), particularly during the soybean podding and maize dough formation stages, played a crucial role in understanding the variations in interannual yield variability. These findings confirmed the importance of GDT in crop yield prediction via machine learning and could be used to facilitate future advancements in machine learning applications for yield forecasting.

Winegrape Yield and Revenue Variability in Australia

While winegrowers usually want to achieve consistent yield targets, there is a high degree of yield and price (and hence gross revenue) variability in winegrape production. The aim of this study was to determine whether there are differences in yield and revenue variability across climates, varieties, and regions in Australia. This was performed by estimating statistical models of the impact of these three variables on the coefficient of variation of yield and gross revenue per hectare. The results suggest that hotter and drier regions exhibit lower interannual yield variability, something that in the past may have been largely explained by the use of irrigation, but which may change in the future with climate change and higher water prices. The results also showed that there are sometimes differences in yield and revenue variability, not only across regions, but also between varieties.

Dependence of the Pea Grain Yield on Climatic Factors under Semi-Arid Conditions

Field peas are one of the most common crops and are grown in various climatic zones. However, the productivity of this crop can be largely limited by climatic factors. This study investigated the influence of climatic factors on pea grain yield in the semi-arid conditions of the Rostov region of Russia in 2008–2020. To quantify climatic factors, agro-climatic variables were used, such as total temperatures below the minimum temperature, the number of days with temperatures below the minimum temperature, total temperatures above the critical temperature, the number of days with temperatures above the critical temperature, and the Selyaninov hydrothermal coefficient. Agro-climatic variables were calculated using daily climatic variables, such as maximum and minimum temperatures, relative air humidity, and precipitation during pea growing season (April–June). The yield of the pea varied from 90 to 250 kg/ha. In general, the productivity of peas is negatively affected by high temperatures and low humidification level. The yield is negatively correlated with accumulative temperatures above the critical temperature and the number of days with temperatures above the critical temperature and positively correlated with the Selyaninov hydrothermal coefficient and the precipitation in all analyzed areas. The influence of the accumulative temperatures above the critical temperature is the most significant. It explains between 6.6% and 78.9% of the interannual variability of the pea yield. The increase in accumulative temperatures above the critical threshold by every 1 °C will contribute to a decrease in pea grain yield by an average of 0.150 kg/ha. The maximum temperatures in May and June (the period of flowering–grain filling) have the most negative impact on the yield. A 1 °C increase in the average maximum temperature during this period will contribute to a decrease in pea yield by an average of 19.175 kg/ha. The influence of total precipitation during the growing season explains between 12.3% and 50.0% of the variability. The 1 mm decrease in the total precipitation for the growing season will lead to a decrease in pea yields by an average of 0.736 kg/ha. The results of this study can be applied to regional yield forecasting, as well as predicting the impact of climate variability on the grain yield of pea crops in arid areas.

Differential responses of crop yields to multi-timescale drought in mainland China: Spatiotemporal patterns and climate drivers

Increasingly frequent and severe droughts pose a growing threat to food security in China. However, our understanding of how different crops respond to multi-timescale drought under varying climatic conditions remains limited, hindering effective drought risk management. To address this knowledge gap, we applied spatial principal component analysis (SPCA) to unveil spatiotemporal patterns in annual yields of major grain crops (rice, wheat, maize) and cotton in response to multi-timescale drought, as indicated by the standardized precipitation evapotranspiration index (SPEI) across China. Subsequently, predictive discriminant analysis (PDA) was employed to identify the primary climatic factors driving these response patterns. The findings indicated that drought-induced interannual variability of crop yields were spatially and temporally heterogeneous, closely tied to the timescale used for drought assessment. Crop types displayed distinct responses to drought, evident in the variations of months and corresponding timescales for their strongest reactions. The initial three principal components, capturing over 65 % of drought-related yield variance, unveiled short- to medium-term patterns for rice, maize, and cotton, and long-term patterns for wheat. Specifically, rice was highly susceptible to drought on a 4-month timescale in September, wheat on a 6-month timescale in May, maize on a 3-month timescale in August, and cotton on a 3-month timescale in September. Moreover, the first three discriminant functions explaining over 90 % of the total variance, effectively distinguish spatiotemporal crop yield response patterns to drought. These patterns primarily stem from seasonal climatic averages, with water balance (precipitation minus potential evapotranspiration) and temperature being the most influential variables (p < 0.05). Interestingly, we observed a weak correlation between drought severity and crop yield in humid conditions, with responses tending to manifest over longer timescales. These findings enhance our comprehension of how drought timescales impact crop yields in China, providing valuable insights for the implementation of rational irrigation management strategies.

Modeling the effect of soil fertility management options on maize yield stability under variable climate in a sub-humid zone in Ghana

IntroductionUncertainty in the yield of maize due to variability in weather is a major challenge to smallholder farmers in Sub Sahara Africa. This study explores the potential of combining locally available organic resources and inorganic fertilizer to increase grain yield and reduce variability in yields associated with variations in rainfall distribution.MethodsTo assess the effectiveness of this practice, the Agricultural Production Systems sIMulator (APSIM) crop model was calibrated and evaluated using maize experiments on nutrient management options. The evaluated model was used to simulate maize growth and yield using multiple-year data (1984–2018) under different planting windows for two growing seasons. The treatments were (i) control, (ii) inorganic fertilizer alone (INOFRecom), combining organic resources [empty fruit bunch of palm (EFB) and compost with inorganic fertilizer (INOF) to make up equivalent nutrients in (ii)]; (iii) EFB + INOF and (iv) Comp + INOF.ResultsThough all the soil amendments boosted grain output, the EFB + INOF treatment outperformed the other treatments in the major season with gains of between 161 and 211% and the most stable (least inter-annual variability of 27%) yield. Across the planting windows, the INOFRecom and EFB + INOF treatments achieved comparable yield increments in the minor season. Though grain yield variability was high during the minor rainy season, combining organic and inorganic fertilizers reduced inter-annual yield variability, thus, lowering uncertainty in yield due to variable inter-annual rainfall. Combining local organic resources with a reduced amount of inorganic fertilizer produced higher yields and better yield stability compared to using only inorganic fertilizer.DiscussionsThus, such soil fertility management solutions might sustain resource use and boost maize grain yield in the study area, where strategies for sustainable crop nutrition remain a critical necessity. The improved nitrogen management regimes may result in fewer environmental hazards for vulnerable rainfed agricultural systems.

Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach

Abstract. Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 % inter-annual differences in recorded yields and up to 17 % inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 % inter-annual differences in recorded yields and up to 5 % in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.

Diverse water management in a preceding wheat crop does not affect maize yield but increases inter-annual variability: A six-year field study

As maize-based crop rotation systems are essential to ensure global food security, understanding the effect of the preceding crops and the associated management on the subsequent maize is crucial. Here we address this view with a 6-year field experiment in the North China Plain's annual winter wheat and summer maize rotation system. Effects of preceding wheat with three water management (conventional irrigation, Con.W/M; optimized irrigation, Opt.W/M; minimum irrigation, Min.W/M) on maize yield and water consumption are investigated. We find that water management in the preceding wheat crop does not affect the subsequent maize yield but increases the interannual yield variability. Over the six years, an average yield of > 10.7 Mg ha-1 is achieved for maize in the three treatments. The highest coefficient of variation (CV) of yield is observed in Min.W/M (12.0%), while there is no significant difference in CV between Con.W/M and Opt.W/M. The annual water consumption follows a decreasing trend in the order of Con.W/M (357 mm), Opt.W/M (332 mm), and Min.W/M (290 mm), which primarily attribute to variations in initial water storage in the deeper soil profile (below 80 cm). The variation in water storage would be eliminated during the following maize growing stages due to ample rainfall in the temperate continental monsoon climate. The findings show that kernel weight (KW) is the primary determinant (accounting for 55–86%) to the observed variations in maize yield. In the Min.W/M treatment, the interactive effect of killing degree days, growing degree days, maximum temperature, and solar radiation accounts for 85% of the variation observed in KW. This study provides insights into the effect of the preceding wheat on the subsequent maize for grain yield and water consumption in the rotation system.

Wheat crop traits conferring high yield potential may also improve yield stability under climate change

Abstract Increasing genetic wheat yield potential is considered by many as critical to increasing global wheat yields and production, baring major changes in consumption patterns. Climate change challenges breeding by making target environments less predictable, altering regional productivity and potentially increasing yield variability. Here we used a crop simulation model solution in the SIMPLACE framework to explore yield sensitivity to select trait characteristics (radiation use efficiency [RUE], fruiting efficiency and light extinction coefficient) across 34 locations representing the world’s wheat-producing environments, determining their relationship to increasing yields, yield variability and cultivar performance. The magnitude of the yield increase was trait-dependent and differed between irrigated and rainfed environments. RUE had the most prominent marginal effect on yield, which increased by about 45 % and 33 % in irrigated and rainfed sites, respectively, between the minimum and maximum value of the trait. Altered values of light extinction coefficient had the least effect on yield levels. Higher yields from improved traits were generally associated with increased inter-annual yield variability (measured by standard deviation), but the relative yield variability (as coefficient of variation) remained largely unchanged between base and improved genotypes. This was true under both current and future climate scenarios. In this context, our study suggests higher wheat yields from these traits would not increase climate risk for farmers and the adoption of cultivars with these traits would not be associated with increased yield variability.

Simulation of crop yield using the global hydrological model H08 (crp.v1)

Abstract. A better understanding of the food–water nexus requires the development of an integrated model that can simultaneously simulate food production and the requirements and availability of water resources. H08 is a global hydrological model that considers human water use and management (e.g., reservoir operation and crop irrigation). Although a crop growth sub-model has been included in H08 to estimate the global crop-specific calendar, its performance as a yield simulator is poor, mainly because a globally uniform parameter set was used for each crop type. In addition, the effects of CO2 fertilization and vapor pressure deficit on crop yield were not considered. Here, through country-wise parameter calibration and algorithm improvement, we enhanced H08 to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. The simulated crop yield was compared with the Food and Agriculture Organization (FAO) national yield statistics and the global dataset of historical yield for major crops (GDHY) gridded yield estimates with respect to mean bias (across nations) and time series correlation (for individual nations). Our results showed that the effects of CO2 fertilization and vapor pressure deficit had opposite impacts on crop yield. The simulated yield showed good consistency with FAO national yield. The mean biases of the major producer countries were considerably reduced to 2 %, 2 %, −2 %, and −1 % for maize, wheat, rice, and soybean, respectively. The capacity of our model to capture the interannual yield variability observed in FAO yield was limited, although the performance of our model was comparable to that of other mainstream global crop models. The grid-level analysis showed that our model showed a similar spatial pattern to that of the GDHY yield in terms of reproducing the temporal variation over a wide area, although substantial differences were observed in other places. Using the enhanced model, we quantified the contributions of irrigation to global food production and compared our results to an earlier study. Overall, our improvements enabled H08 to estimate crop production and hydrology in a single framework, which will be beneficial for global food–water nexus studies in relation to climate change.

Copernicus global crop productivity indicators: An evaluation based on regionally reported yields

Climate variability places significant pressure on climate-sensitive sectors such as agriculture. Information on crop-related climate and water indicators under climate change becomes essential. The Copernicus Climate Change Service (C3S) provides reliable information about climate change based on climate models, Earth Observations, and in situ data to support adaptation policies. Within this C3S context, the Global Agriculture Sectoral Information System (SIS) has developed a new product based on global crop productivity indicators to document the effects of climate variability on crop yield variability. This study focuses on the evaluation of climate-enhanced, earth observation (EO) based crop productivity indicators i.e., Total Weight Storage Organs (TWSO, equivalent to yield), Total Above Ground Production (TAGP). These variables provide insights into the Spatio-temporal variability of yield, productivity, and development of four major staple crops (rice, maize, soybean, and wheat) for their main production regions at the global scale for the period 2000–2018. In this study, the evaluation was carried out spatially and temporally for the United States of America, India, and China for the period 2000–2018, using reported yield statistics aggregated to the lowest available administrative level of each region. For almost all the crops in the three countries, the skilled error can be reduced by more than 25% for both the indicators TAGP and TWSO compared to the trend of reported yield, suggesting considerable performance in assessing interannual yield variability. Results indicated both indicators individually performed well in many important producing areas of the USA, India, and China. However, if we compare both the indicator’s performance in terms of production totals in the USA, India, and China, TAGP contributes ∼27–50% of total crop production in the USA and India, and ∼47–75% in China. While, TWSO contributes ∼24–41% in the USA and India, and 5–65% in China.

Development and the Effect of Weather and Mineral Fertilization on Grain Yield and Stability of Winter Wheat following Alfalfa—Analysis of Long-Term Field Trial

Within the framework of a long-term experiment, established in 1955, we evaluated the development and effects of weather and mineral fertilization (Control, NPK1, NPK2, NPK3, NPK4) on the yield and stability of winter wheat following alfalfa. In total, 19 seasons were analysed. The weather changed considerably at the experimental site. Significant increases in minimal, mean, and maximal temperatures were dated to the period 1987-1988, while precipitation remained the same to this day (insignificantly increasing trend by 0.5 mm per annum). Rising temperatures in November, May, and July positively affected wheat grain yield, especially in treatments with higher N doses. No relationship between yield and precipitation was recorded. Highest inter-annual yield variability was recorded in Control and NPK4 treatments. Although minerally fertilized treatments provided slightly higher yields, the difference between Control and NPK treatments was insignificant. According to the linear-plateau response model, the recommended dose of 44 kg ha-1 N corresponds with yield of 7.4 t ha-1, while Control provides an average yield of 6.8 t ha-1. The application of higher doses did not lead to significant grain yield increase. Alfalfa as a preceding crop reduces the need of N fertilization and contributes to sustainable conventional agriculture, however, its share in crop rotations is decreasing both in the Czech Republic and in Europe.

Can green manure contribute to sustainable intensification of rainfed rice production in Madagascar?

Green manure can be a source of nutrients for crops to spare some of the mineral fertilizer required for sustainable intensification of agriculture in smallholder context. We explored with a calibrated model the potential of legume green manure to help spare mineral fertilizer for sustainable intensification of rainfed rice production in Madagascar highlands. This study builds on a two-year rice experiment with rice and green manure as previous crop. The experiment investigated the response of rainfed rice growth to the incorporation into the soil of i) rice residue only (C:N ratio of 44.9) and ii) mucuna + crotalaria green manures (C:N ratio of 18.7). Two levels of fertilizer were investigated: manure only (5 t DM ha −1 ) (F1) and manure (5 t DM ha −1 ) in combination with mineral fertilizer (136 kg N ha −1 ) (F2). The crop model STICS was calibrated against observed soil water and soil mineral N, plant N uptake, aboveground biomass and grain yield to reproduce the impact of residue type and fertilizer input on mineralization of organic N in soil and in crop residue, crop growth and grain yield. The calibrated crop model was then used to explore rice response to mineral N fertilizer, with or without green manure, for the historical climate (1994–2018). Relative Root Mean Square Error (rRMSE) of model simulations fell within the observed relative variation in grain yield due to treatment effect. With green manure, mineral N available for rice increased compared with the situation without green manure, leading to greater agronomic use efficiency of N fertilizer. Green manure helped spare mineral fertilizer to achieve a reference yield set at 75 % of potential yield. This reference yield (3.67 t ha −1 on average across years) was reached with 40 kg N ha −1 with 6 t ha −1 of green manure, and 100 kg N ha −1 without green manure. The analysis of rice yield inter-annual variability indicated that (i) N leaching increased with total seasonal rainfall, which undermined the benefits of green manure on rice productivity in wetter years, and (ii) the increase in rice productivity allowed by the green manure did not fully compensate the rice yield foregone with the cultivation of the green manure. The calibrated model can be a useful tool to explore additional management strategies that will help maximize the benefit of green manure for sustainable intensification of rice-based cropping systems, especially in low-input cropping systems. • Impacts of green manure incorporation into the soil was assessed using modeling approach. • Rice response to mineral N fertilizer and green manure was evaluated with historical climate. • N available for crop increased with the use of green manure. • Green manure helped spare mineral fertilizer to achieve potential yield. • Benefits of green manure is undermined in wet years partly due to N leaching.

Climate service driven adaptation may alleviate the impacts of climate change in agriculture

Building a resilient and sustainable agricultural sector requires the development and implementation of tailored climate change adaptation strategies. By focusing on durum wheat (Triticum turgidum subsp. durum) in the Euro-Mediterranean region, we estimate the benefits of adapting through seasonal cultivar-selection supported by an idealised agro-climate service based on seasonal climate forecasts. The cost of inaction in terms of mean yield losses, in 2021–2040, ranges from −7.8% to −5.8% associated with a 7% to 12% increase in interannual variability. Supporting cultivar choices at local scale may alleviate these impacts and even turn them into gains, from 0.4% to 5.3%, as soon as the performance of the agro-climate service increases. However, adaptation advantages on mean yield may come with doubling the estimated increase in the interannual yield variability.

Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model

Abstract A major challenge for food security worldwide is the large interannual variability of crop yield, and climate change is expected to further exacerbate this volatility. Accurate prediction of the crop response to climate variability and change is critical for short-term management and long-term planning in multiple sectors. In this study, using maize in the U.S. Corn Belt as an example, we train and validate multiple machine learning (ML) models predicting crop yield based on meteorological variables and soil properties using the leaving-one-year-out approach, and compare their performance with that of a widely used process-based crop model (PBM). Our proposed long short-term memory model with attention (LSTMatt) outperforms other ML models (including other variations of LSTM developed in this study) and explains 73% of the spatiotemporal variance of the observed maize yield, in contrast to 16% explained by the regionally calibrated PBM; the magnitude of yield prediction errors in LSTMatt is about one-third of that in the PBM. When applied to the extreme drought year 2012 that has no counterpart in the training data, the LSTMatt performance drops but still shows advantage over the PBM. Findings from this study suggest a great potential for out-of-sample application of the LSTMatt model to predict crop yield under a changing climate. Significance Statement Changing climate is expected to exacerbate extreme weather events, thus affecting global food security. Accurate estimation and prediction of crop productivity under extremes are crucial for long-term agricultural decision-making and climate adaptation planning. Here we seek to improve crop yield prediction from meteorological features and soil properties using machine learning approaches. Our long short-term memory (LSTM) model with attention and shortcut connection explains 73% of the spatiotemporal variance of the observed maize yield in the U.S. Corn Belt and outperforms a widely used process-based crop model even in an extreme drought year when meteorological conditions are significantly different from the training data. Our findings suggest great potential for out-of-sample application of the LSTM model to predict crop yield under a changing climate.

Responses of Wheat Yield under Different Fertilization Treatments to Climate Change Based on a 35-Year In Situ Experiment

Fertilization, as one of many important field management practices, can increase crop yields. However, whether different levels of fertilization will affect the response of wheat yields to inter-annual climate variations and long-term climate trends is not clear. In this study, 35-year wheat yields were used to investigate the responses of wheat yield to inter-annual climate variations and long-term climate trends under different fertilization treatments. The first difference method was used to de-trend wheat yields and climate variables and stepwise regression analysis was used to quantify the yield–climate relationship. The experimental design consisted of a control treatment (CK without fertilization) and three fertilizer treatments: nitrogen, phosphorus, and manure (NPM with 120 kg ha−1 N, 26.2 kg ha−1 P, and 75 t ha−1 manure), nitrogen and phosphorus (NP with 120 kg ha−1 N and 26.2 kg ha−1 P), and manure (M with 75 t ha−1 manure). Compared to the CK treatment, the NPM, NP, and M treatments increased wheat yield by an average of 201.9, 161.7, and 130.6% and increased yield inter-annual variability by an average of 191.2, 149.3, and 144.2%, respectively, during the study period (1985–2020). Inter-annual climate fluctuations in the study area explained 45, 38, 27, and 29% of wheat yield variations and 35-year climatic trends contributed to wheat yield decreases of 0.3, 0.7, 1.6, and 1.8% for the NPM, NP, M, and CK treatments, respectively. The results show the impact of inter-annual climate fluctuations on yield increases with the increasing level of fertilization, while the effect of long-term climate trends on yield decreases with the increasing level of fertilization.

Improving the representation of sugarcane crop in the Joint UK Land Environment Simulator (JULES) model for climate impact assessment

AbstractBioenergy from sugarcane production is considered a key mitigation strategy for global warming. Improving its representation in land surface models is important to further understand the interactions between climate and bioenergy production, supporting accurate climate projections and decision‐making. This study aimed to calibrate and evaluate the Joint UK Land Environment Simulator (JULES) for climate impact assessments in sugarcane. A dataset composed of soil moisture, water and carbon fluxes and biomass measurements from field experiments across Brazil was used to calibrate and evaluate JULES‐crop and JULES‐BE parametrizations. The ability to predict the spatiotemporal variability of sugarcane yields and the impact of climate change was also tested at five Brazilian microregions. Parameters related to sugarcane allometry, crop growth and development were derived and tested for JULES‐crop and JULES‐BE, together with the response to atmospheric carbon dioxide, temperature and low‐water availability (CTW‐response). Both parametrizations showed comparable performance to other sugarcane dynamic models, with a root mean squared error of 6.75 and 6.05 t ha−1 for stalk dry matter for JULES‐crop and JULES‐BE, respectively. The parametrizations were also able to replicate the average yield patterns observed in the five microregions over 30 years when the yield gap factors were taken into account, with the correlation (r) between simulated and observed interannual variability of yields ranging from 0.33 to 0.56. An overall small positive trend was found in future yield projections of sugarcane using the new calibrations, with exception of the Jataí mesoregion which showed a clear negative trend for the SSP5 scenario from the years 2070 to 2100. Our simulations showed that an abrupt negative impact on sugarcane yields is expected if daytime temperatures above 35°C become more frequent. The newly calibrated version of JULES can be applied regionally and globally to help understand the interactions between climate and bioenergy production.

Dependence of maize yield on hydrothermal factors in various agro-climatic zones of the Rostov region of Russia in the context of climate change.

Trends in mean monthly temperature and precipitation during the growing season and their effects on the maize yield were analyzed at the Zimovnikovsky (Zim) and Rostov (Ros) state variety plots (SVPs), located in different agro-climatic zones of the Rostov region. For these two SVPs, in the period of 1975-2019, the Mann-Kendall test showed a statistically significant increase (p < 0.05) in mean temperature (0.70 and 0.52°C/decade) and a trend of decreased total precipitation (- 14.81 and - 10.40mm/decade) during the maize growing season. The dependence of the maize yield on hydrothermal factors was estimated for the period of 2011-2019 using the Pearson correlation coefficient (p < 0.05). The mean temperature in September at Zim negatively (r = - 0.78), and in June at Ros positively (r = 0.77) correlated with yield, which explained, according to the value of the coefficient of determination (R2), up to 60.7% and 58.7%, respectively, of the interannual variability of the maize yield. The precipitation in July at the Zim and Ros positively correlated (r = 0.75 and r = 0.71) with yield and explained up to 55.9% and 50.6%, respectively, of the interannual variability of the maize yield. The total amount of precipitation during the growing season at Zim was the dominant factor, explaining up to 75.7% of the interannual variability of maize yield. The continuation of the observed climatic trends during the growing season could lead in the next decade to both a decrease in the maize yield by an average of 0.25 t/ha at Zim and an increase in the maize yield by an average of 0.42 t/ha at Ros.

Future climate change significantly alters interannual wheat yield variability over half of harvested areas

Climate change affects the spatial and temporal distribution of crop yields, which can critically impair food security across scales. A number of previous studies have assessed the impact of climate change on mean crop yield and future food availability, but much less is known about potential future changes in interannual yield variability. Here, we evaluate future changes in relative interannual global wheat yield variability (the coefficient of variation (CV)) at 0.25° spatial resolution for two representative concentration pathways (RCP4.5 and RCP8.5). A multi-model ensemble of crop model emulators based on global process-based models is used to evaluate responses to changes in temperature, precipitation, and CO2. The results indicate that over 60% of harvested areas could experience significant changes in interannual yield variability under a high-emission scenario by the end of the 21st century (2066–2095). About 31% and 44% of harvested areas are projected to undergo significant reductions of relative yield variability under RCP4.5 and RCP8.5, respectively. In turn, wheat yield is projected to become more unstable across 23% (RCP4.5) and 18% (RCP8.5) of global harvested areas—mostly in hot or low fertilizer input regions, including some of the major breadbasket countries. The major driver of increasing yield CV change is the increase in yield standard deviation, whereas declining yield CV is mostly caused by stronger increases in mean yield than in the standard deviation. Changes in temperature are the dominant cause of change in wheat yield CVs, having a greater influence than changes in precipitation in 53% and 72% of global harvested areas by the end of the century under RCP4.5 and RCP8.5, respectively. This research highlights the potential challenges posed by increased yield variability and the need for tailored regional adaptation strategies.

ENSO influence on corn and soybean yields as a base of an early warning system for agriculture in Córdoba, Argentina

Reducing uncertainty and productive risk increase the resilience of agricultural systems and food security of a region. While crop productivity strongly depends on weather conditions during the growing season, El Niño Southern Oscillation (ENSO) phenomenon is the main driver of global interannual climate variability and affects crop production in many regions. The ENSO, Pacific Decadal Oscillation (PDO) and Indian Ocean Dipole (IOD) impact on corn and soybean yields between 1973 and 2017 was compared in Córdoba Province, Argentina, looking for an early signal to support agricultural activity. The analysis suggests that not only ENSO signal (SOI and SST El Niño 3.4) but also PDO anomaly (PDOA) link better than IOD with productivity for both crops, as well as the Southern Oscillation Index of August and September (SOIas) is a proper yield predictor. An inverse relationship was determined between SOIas and yields anomaly, more spread territorially for soybeans since the linear functions are significant (p < 0.05) in 9 of the 12 administrative areas and, with a slightly lower significance (p < 0.1), in the remaining too. On the other hand, the low R2 values indicate little capacity to explain the yield interannual variability from the SOIas signal that for corn, has a departmental range between 2 % and 19 % and, for soybeans, slightly higher between 7 % and 24 %. While the mean difference test of yield anomaly between different ENSO phases (El Niño, La Niña and Neutral) supports the more general indifference of both extreme phases with the neutral cases, it reaches a significant character in 8 departments when the cold phase is compared with the warm. The seasonal rainfall regime has also a reduced capacity to explain productive variability. However, this information is useful to realize that both the wettest (El Niño) and the driest years (La Niña) fail to differentiate from those with normal rainfall. This confusion was verified by a combinatory frequency analysis of rainfall and productivity data related to the ENSO phases. Because ENSO allows anticipating the rainfall condition and the productive potential of the main rain-fed crops in Córdoba, Argentina, it constitutes appropriate information to reduce uncertainty and the risk level in agricultural production. Therefore, although the ENSO predictive capacity is limited and more knowledge about the influence of other climatic variability sources like PDO is needed, its incorporation into a climate monitoring protocol must conforms to the basis of an early warning system for a sustainable agriculture in the region.

Night Temperature Determines the Interannual Yield Variation in Hybrid and Inbred Rice Widely Used in Central China Through Different Effects on Reproductive Growth.

Interannual variation in grain yield of rice has been observed at both farm and regional scales, which is related to the climate variability. Previous studies focus on predicting the trend of climate change in the future and its potential effects on rice production using climate models; however, field studies are lacking to examine the climatic causes underlying the interannual yield variability for different rice cultivars. Here a 6-year field experiment from 2012 to 2017 was conducted using one hybrid (Yangliangyou6, YLY6) cultivar and one inbred (Huanghuazhan, HHZ) cultivar to determine the climate factors responsible for the interannual yield variation. A significant variation in grain yield was observed for both the inbred and hybrid cultivars across six planting years, and the coefficient of variation for grain yield was 7.3–10.5%. The night temperature (average daily minimum temperature, Tmin) contributed to the yield variability in both cultivars. However, the two cultivars showed different responses to the change in Tmin. The yield variation in HHZ was mainly explained by the effects of Tmin on grain filling percentage and grain weight, while the change in spikelets m−2 in response to Tmin accounted for the yield variability in YLY6. Further analysis found that spikelets m−2 of YLY6 significantly and negatively correlated with Tmin from transplanting to heading. For HHZ, the grain filling percentage and grain weight were significantly affected by Tmin of the week prior to heading and from heading to maturity, respectively. Overall, there were differences in the response mechanism between hybrid and inbred cultivars to high night temperature. These will facilitate the development of climate-resilient cultivars and appropriate management practices to achieve a stable grain yield.