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Timely and accurate forecasting of crop yields is crucial to food security and sustainable development in the agricultural sector. However, winter wheat yield estimation and forecasting on a regional scale still remains challenging. In this study, we established a two-branch deep learning model to predict winter wheat yield in the main producing regions of China at the county level. The first branch of the model was constructed based on the Long Short-Term Memory (LSTM) networks with inputs from meteorological and remote sensing data. Another branch was constructed using Convolution Neural Networks (CNN) to model static soil features. The model was then trained using the detrended statistical yield data during 1982 to 2015 and evaluated by leave-one-year-out-validation. The evaluation results showed a promising performance of the model with the overall R 2 and RMSE of 0.77 and 721 kg/ha, respectively. We further conducted yield prediction and uncertainty analysis based on the two-branch model and obtained the forecast accuracy in one month prior to harvest of 0.75 and 732 kg/ha. Results also showed that while yield detrending could potentially introduce higher uncertainty, it had the advantage of improving the model performance in yield prediction.

Rice is generally grown under completely flooded condition and providing food for more than half of the world’s population. Any changes in weather parameters might affect the rice productivity thereby impacting the food security of burgeoning population. So, the crop yield forecasting based on weather parameters will help farmers, policy makers and administrators to manage adversities. The present investigation examines the application of stepwise multiple linear regression (SMLR), artificial neural network (ANN) solely and in combination with principal components analysis (PCA) and penalised regression models (e.g. least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)) for rice yield prediction using long-term weather data. The R2 and root mean square error (RMSE) of the models varied between 0.22–0.98 and 24.02–607.29 kg ha−1, respectively during calibration. During validation with independent dataset, the RMSE and normalised root mean square error (nRMSE) ranged between 21.35–981.89 kg ha−1 and 0.98–36.7%, respectively. For evaluation of multiple models for multiple locations statistically, overall average ranks on the basis of R2 and RMSE of calibration; RMSE and nRMSE of validation were calculated and non-parametric Friedman test was applied to check the significant difference among the models. The ranking of the models revealed that LASSO (2.63) was the best performing model followed by ENET (3.07) while PCA-ANN (4.19) was the worst model which was found significant at p < 0.001. The reason behind good performance of LASSO and ENET is that these models prevent overfitting and reduce model complexity by penalising the magnitude of coefficients. Then, pairwise multiple comparison test was performed which indicated LASSO as the best model which was found similar to SMLR and ENET. So, for prediction of rice yield, these models can very well be utilised for west coast of India.

A key concern in agriculture is how to feed the expanding population and safeguard the environment from the ill effects of climate change. To feed a growing global population, food production and security are significant problems, as food output may need to double by 2050. Thus, more innovative and effective approaches for increasing agricultural productivity (hence, food production) are required to meet the rising demand for food. The world’s most widely cultivated grains include corn, wheat, and rice, which serve as the foundation for basic foods. This review focuses on some of the key most up-to-date approaches that boost wheat, rice, corn, barley, and oat yields with insight into how molecular technology and genetics may raise the production and resource-efficient use of these important grains. Although red light management and genetic manipulation show maximal grain yield enhancement, other covered strategies including bacterial-nutrient management, solar brightening, facing abiotic stress through innovative agricultural systems, fertilizer management, harmful gas emissions reduction, photosynthesis enhancement, stress tolerance, disease resistance, and varietal improvement also enhance grain production and increase plant resistance to harmful environmental circumstances. This study also discusses the potential challenges of the addressed approaches and possible future perspectives.

[This corrects the article DOI: 10.1371/journal.pone.0124127.].

Soybean, belonging to legumes, has a specific ability to biological nitrogen fixation, which can be reinforced by seeds inoculation. However, support with a starter dose of mineral nitrogen fertilizer may be necessary to achieve high seed yields. A four-year field experiment was conducted to determine the effect of mineral N fertilization (0, 30, 60 kg ha −1 ), seed inoculation with two commercial inoculants and combinations of these treatments on yield components and yielding of soybean in conditions of south-western part of Poland. The synergistic effect of mineral fertilization at dose 30 kg ha −1 and inoculation on soybean productivity was the most beneficial. Similar effects were observed when 60 kg N ha −1 was applied both separately and with inoculation. However, due to the environmental impact of mineral fertilizers and to promote plants to biological nitrogen fixation (BNF), it is advisable to use lower doses of N fertilizer (at 30 kg ha −1 ) and inoculate soybean seeds in agro- climatic conditions of south-western Poland. Therefore, based on this study we recommend to apply starter dose of N and inoculation.

Reliable and timely crop-yield prediction and crop mapping are crucial for food security and decision making in the food industry and in agro-environmental management. The global coverage, rich spectral and spatial information and repetitive nature of remote sensing (RS) data have made them effective tools for mapping crop extent and predicting yield before harvesting. Advanced machine-learning methods, particularly deep learning (DL), can accurately represent the complex features essential for crop mapping and yield predictions by accounting for the nonlinear relationships between variables. The DL algorithm has attained remarkable success in different fields of RS and its use in crop monitoring is also increasing. Although a few reviews cover the use of DL techniques in broader RS and agricultural applications, only a small number of references are made to RS-based crop-mapping and yield-prediction studies. A few recently conducted reviews attempted to provide overviews of the applications of DL in crop-yield prediction. However, they did not cover crop mapping and did not consider some of the critical attributes that reveal the essential issues in the field. This study is one of the first in the literature to provide a thorough systematic review of the important scientific works related to state-of-the-art DL techniques and RS in crop mapping and yield estimation. This review systematically identified 90 papers from databases of peer-reviewed scientific publications and comprehensively reviewed the aspects related to the employed platforms, sensors, input features, architectures, frameworks, training data, spatial distributions of study sites, output scales, evaluation metrics and performances. The review suggests that multiple DL-based solutions using different RS data and DL architectures have been developed in recent years, thereby providing reliable solutions for crop mapping and yield prediction. However, challenges related to scarce training data, the development of effective, efficient and generalisable models and the transparency of predictions should be addressed to implement these solutions at scale for diverse locations and crops.

Quantitative understanding of factors driving yield increases of major food crops is essential for effective prioritization of research and development. Yet previous estimates had limitations in distinguishing among contributing factors such as changing climate and new agronomic and genetic technologies. Here, we distinguished the separate contribution of these factors to yield advance using an extensive database collected from the largest irrigated maize-production domain in the world located in Nebraska (United States) during the 2005-to-2018 period. We found that 48% of the yield gain was associated with a decadal climate trend, 39% with agronomic improvements, and, by difference, only 13% with improvement in genetic yield potential. The fact that these findings were so different from most previous studies, which gave much-greater weight to genetic yield potential improvement, gives urgency to the need to reevaluate contributions to yield advances for all major food crops to help guide future investments in research and development to achieve sustainable global food security. If genetic progress in yield potential is also slowing in other environments and crops, future crop-yield gains will increasingly rely on improved agronomic practices.

Predicting wheat yield is crucial due to the importance of wheat across the world. When modeling yield, the difference between potential and actual yield consistently changes because of advances in technology. Considering historical yield potential would help determine spatiotemporal trends in agricultural development. Comparing current and historical yields in Denmark is possible because yield potential has been documented throughout history. However, the current national winter wheat yield map solely uses soil properties within the model. The aim of this study was to generate a new Danish winter wheat yield map and compare the results to historical yield potential. Utilizing random forest with soil, climate, and topography variables, a winter wheat yield map was generated from 876 field trials carried out from 1992 to 2018. The random forest model performed better than the model based only on soil. The updated national yield map was then compared to yield potential maps from 1688 and 1844. While historical time periods are characterized by numerous low yield potential areas and few highly productive areas, current yield is evenly distributed between low and high yields. Advances in technology and farm practices have exceeded historical yield predictions, mainly due to the use of fertilizer, irrigation, and drainage. Thus, modeling yield projections could be unreliable in the future as technology progresses.

There is a critical need for sensitive remote sensing approaches to monitor the parameters governing photosynthesis, at the temporal scales relevant to their natural dynamics. The photochemical reflectance index (PRI) and chlorophyll fluorescence (F) offer a strong potential for monitoring photosynthesis at local, regional, and global scales, however the relationships between photosynthesis and solar induced F (SIF) on diurnal and seasonal scales are not fully understood. This study examines how the fine spatial and temporal scale SIF observations relate to leaf level chlorophyll fluorescence metrics (i.e., PSII yield, YII and electron transport rate, ETR), canopy gross primary productivity (GPP), and PRI. The results contribute to enhancing the understanding of how SIF can be used to monitor canopy photosynthesis. This effort captured the seasonal and diurnal variation in GPP, reflectance, F, and SIF in the O2A (SIFA) and O2B (SIFB) atmospheric bands for corn (Zea mays L.) at a study site in Greenbelt, MD. Positive linear relationships of SIF to canopy GPP and to leaf ETR were documented, corroborating published reports. Our findings demonstrate that canopy SIF metrics are able to capture the dynamics in photosynthesis at both leaf and canopy levels, and show that the relationship between GPP and SIF metrics differs depending on the light conditions (i.e., above or below saturation level for photosynthesis). The sum of SIFA and SIFB (SIFA+B), as well as the SIFA+B yield, captured the dynamics in GPP and light use efficiency, suggesting the importance of including SIFB in monitoring photosynthetic function. Further efforts are required to determine if these findings will scale successfully to airborne and satellite levels, and to document the effects of data uncertainties on the scaling.

The United States (US) is the largest alfalfa ( Medicago sativa L.) producer in the world. More than 44% of the US alfalfa is produced under rainfed conditions, although it requires a relatively high amount of water compared to major field crops. Considering that yield and production of rainfed alfalfa have been relatively stagnant in the country for decades, there is a need to better understand the magnitude of yield loss due to water limitation and how far from yield potential current yields are. In this context, the main objective of this study was to estimate the current yield gap of rainfed alfalfa in the US. We collected 10 year (2009–2018) county-level government-reported yield and weather data from 393 counties within 12 major US rainfed alfalfa producing states and delineated alfalfa growing season using probabilistic approaches based on temperature thresholds for crop development. We then calculated county-level growing season rainfall (GSR), which was plotted against county-level yield to determine attainable yield (Ya) using frontier function analysis, and water-limited potential yield (Yw) using boundary function analysis. Average and potential water use efficiencies (WUE) were estimated, and associated yield gap referring to attainable (YGa) or water-limited yields (YGw) were calculated. Finally, we used conditional inference trees (CIT) to identify major weather-related yield-limiting factors to alfalfa forage yield. The frontier model predicted a mean Ya of 9.6 ± 1.5 Mg ha −1 and an associated optimum GSR of 670 mm, resulting in a mean YGa of 34%. The boundary function suggested a mean Yw of 15.3 ± 3 Mg ha −1 at the mean GSR of 672 ± 153 mm, resulting in a mean yield gap of 58%. The potential alfalfa WUE was 30 kg ha −1 mm −1 with associated minimum water losses of 24% of mean GSR, which was three times greater than the mean WUE of 10 kg ha −1 mm −1 . The CIT suggested that GSR and minimum temperature in the season were the main yield-limiting weather variables in rainfed alfalfa production in the US. Our study also revealed that alfalfa was only limited by water availability in 21% of the environments. Thus, future research on management practices to narrow yield gaps at current levels of water supply is necessary.