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The current trajectory for crop yields is insufficient to nourish the world’s population by 2050 1 . Greater and more consistent crop production must be achieved against a backdrop of climatic stress that limits yields, owing to shifts in pests and pathogens, precipitation, heat-waves and other weather extremes. Here we consider the potential of plant sciences to address post-Green Revolution challenges in agriculture and explore emerging strategies for enhancing sustainable crop production and resilience in a changing climate. Accelerated crop improvement must leverage naturally evolved traits and transformative engineering driven by mechanistic understanding, to yield the resilient production systems that are needed to ensure future harvests. Genetic strategies for improving the yield and sustainability of agricultural crops, and the resilience of crops in the face of biotic and abiotic stresses contingent on projected climate change, are evaluated.

Several studies have shown that global crop production needs to double by 2050 to meet the projected demands from rising population, diet shifts, and increasing biofuels consumption. Boosting crop yields to meet these rising demands, rather than clearing more land for agriculture has been highlighted as a preferred solution to meet this goal. However, we first need to understand how crop yields are changing globally, and whether we are on track to double production by 2050. Using ∼2.5 million agricultural statistics, collected for ∼13,500 political units across the world, we track four key global crops-maize, rice, wheat, and soybean-that currently produce nearly two-thirds of global agricultural calories. We find that yields in these top four crops are increasing at 1.6%, 1.0%, 0.9%, and 1.3% per year, non-compounding rates, respectively, which is less than the 2.4% per year rate required to double global production by 2050. At these rates global production in these crops would increase by ∼67%, ∼42%, ∼38%, and ∼55%, respectively, which is far below what is needed to meet projected demands in 2050. We present detailed maps to identify where rates must be increased to boost crop production and meet rising demands.

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.

Photosynthetic pigments are an integral and vital part of all photosynthetic machinery and are present in different types and abundances throughout the photosynthetic apparatus. Chlorophyll, carotenoids and phycobilins are the prime photosynthetic pigments which facilitate efficient light absorption in plants, algae, and cyanobacteria. The chlorophyll family plays a vital role in light harvesting by absorbing light at different wavelengths and allowing photosynthetic organisms to adapt to different environments, either in the long-term or during transient changes in light. Carotenoids play diverse roles in photosynthesis, including light capture and as crucial antioxidants to reduce photodamage and photoinhibition. In the marine habitat, phycobilins capture a wide spectrum of light and have allowed cyanobacteria and red algae to colonise deep waters where other frequencies of light are attenuated by the water column. In this review, we discuss the potential strategies that photosynthetic pigments provide, coupled with development of molecular biological techniques, to improve crop yields through enhanced light harvesting, increased photoprotection and improved photosynthetic efficiency.

Cropping sequence diversification provides a systems approach to reduce yield variations and improve resilience to multiple environmental stresses. Yield advantages of more diverse crop rotations and their synergistic effects with reduced tillage are well documented, but few studies have quantified the impact of these management practices on yields and their stability when soil moisture is limiting or in excess. Using yield and weather data obtained from a 31-year long term rotation and tillage trial in Ontario, we tested whether crop rotation diversity is associated with greater yield stability when abnormal weather conditions occur. We used parametric and non-parametric approaches to quantify the impact of rotation diversity (monocrop, 2-crops, 3-crops without or with one or two legume cover crops) and tillage (conventional or reduced tillage) on yield probabilities and the benefits of crop diversity under different soil moisture and temperature scenarios. Although the magnitude of rotation benefits varied with crops, weather patterns and tillage, yield stability significantly increased when corn and soybean were integrated into more diverse rotations. Introducing small grains into short corn-soybean rotation was enough to provide substantial benefits on long-term soybean yields and their stability while the effects on corn were mostly associated with the temporal niche provided by small grains for underseeded red clover or alfalfa. Crop diversification strategies increased the probability of harnessing favorable growing conditions while decreasing the risk of crop failure. In hot and dry years, diversification of corn-soybean rotations and reduced tillage increased yield by 7% and 22% for corn and soybean respectively. Given the additional advantages associated with cropping system diversification, such a strategy provides a more comprehensive approach to lowering yield variability and improving the resilience of cropping systems to multiple environmental stresses. This could help to sustain future yield levels in challenging production environments.

Intercropping, a widely adopted agricultural practice worldwide, aims to increase crop yield, enhance plant nutrient uptake, and optimize the utilization of natural resources, contributing to sustainable farming practices on a global scale. However, the underlying changes in soil physio-chemical characteristics and enzymatic activities, which contribute to crop yield and nutrient uptake in the intercropping systems are largely unknown. Consequently, a two-year (2021–2022) field experiment was conducted on the maize/soybean intercropping practices with/without nitrogen (N) fertilization (i.e., N 0 ; 0 N kg ha −1 and N 1 ; 225 N kg ha −1 for maize and 100 N kg ha −1 for soybean ) to know whether such cropping system can improve the nutrients uptake and crop yields, soil physio-chemical characteristics, and soil enzymes, which ultimately results in enhanced crop yield. The results revealed that maize intercropping treatments (i.e., N 0 MI and N 1 MI) had higher crop yield, biomass dry matter, and 1000-grain weight of maize than mono-cropping treatments (i.e., N 0 MM, and N 1 MM). Nonetheless, these parameters were optimized in N 1 MI treatments in both years. For instance, N 1 MI produced the maximum grain yield (10,105 and 11,705 kg ha −1 ), biomass dry matter (13,893 and 14,093 kg ha −1 ), and 1000-grain weight (420 and 449 g) of maize in the year 2021 and 2022, respectively. Conversely, soybean intercropping treatments (i.e., N 0 SI and N 1 SI) reduced such yield parameters for soybean. Also, the land equivalent ratio (LER) and land equivalent ratio for N fertilization (LER N ) values were always greater than 1, showing the intercropping system’s benefits in terms of yield and improved resource usage. Moreover, maize intercropping treatments (i.e., N 0 MI and N 1 MI) and soybean intercropping treatments (i.e., N 0 SI and N 1 SI) significantly ( p  < 0.05) enhanced the nutrient uptake (i.e., N, P, K, Ca, Fe, and Zn) of maize and soybean, however, these nutrients uptakes were more prominent in N 1 MI and N 1 SI treatments of maize and soybean, respectively in both years (2021 and 2022) compared with their mono-cropping treatments. Similarly, maize-soybean intercropping treatments (i.e., N 0 MSI and N 1 MSI) significantly ( p  < 0.05) improved the soil-based N, P, K, NH 4 , NO 3 , and soil organic matter, but, reduced the soil pH. Such maize-soybean intercropping treatments also improved the soil enzymatic activities such as protease (PT), sucrose (SC), acid phosphatase (AP), urease (UE), and catalase (CT) activities. This indicates that maize-soybean intercropping could potentially contribute to higher and better crop yield, enhanced plant nutrient uptake, improved soil nutrient pool, physio-chemical characteristics, and related soil enzymatic activities. Thus, preferring intercropping to mono-cropping could be a preferable choice for ecologically viable agricultural development.

Maize is one of the most important crops globally, and it shows remarkable genetic diversity. Knowledge of this diversity could help in crop improvement; however, gold-standard genomes have been elucidated only for modern temperate varieties. Here, we present a high-quality reference genome (contig N50 of 15.78 megabases) of the maize small-kernel inbred line, which is derived from a tropical landrace. Using haplotype maps derived from B73, Mo17 and SK, we identified 80,614 polymorphic structural variants across 521 diverse lines. Approximately 22% of these variants could not be detected by traditional single-nucleotide-polymorphism-based approaches, and some of them could affect gene expression and trait performance. To illustrate the utility of the diverse SK line, we used it to perform map-based cloning of a major effect quantitative trait locus controlling kernel weight—a key trait selected during maize improvement. The underlying candidate gene ZmBARELY ANY MERISTEM1d provides a target for increasing crop yields. A high-quality reference genome of the maize SK inbred line and analyses between the tropical SK line and two other maize genomes, B73 and Mo17, provide insights into structural variation and crop improvement.

A timely and consistent assessment of crop yield will assist the farmers in improving their income, minimizing losses, and deriving strategic plans in agricultural commodities to adopt import-export policies. Crop yield predictions are one of the various challenges faced in the agriculture sector and play a significant role in planning and decision-making. Machine learning algorithms provided enough belief and proved their ability to predict crop yield. The selection of the most suitable crop is influenced by various environmental factors such as temperature, soil fertility, water availability, quality, and seasonal variations, as well as economic considerations such as stock availability, preservation capabilities, market demand, purchasing power, and crop prices. The paper outlines a framework used to evaluate the performance of various machine-learning algorithms for forecasting crop yields. The models were based on a range of prime parameters including pesticides, rainfall and average temperature. The Results of three machine learning algorithms, Categorical Boosting (CatBoost), Light Gradient-Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost) are compared and found more accurate than other algorithms in predicting crop yields. The RMSE and R 2 values were calculated to compare the predicted and observed rice yields, resulting in the following values: CatBoost with 800 (0.24), LightGBM with 737 (0.33), and XGBoost with 744 (0.31). Among these three machine learning algorithms, CatBoost demonstrated the highest precision in predicting yields, achieving an accuracy rate of 99.123%.