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Especially in low-income nations, new and orphan crops provide important opportunities to improve diet quality and the sustainability of food production, being rich in nutrients, capable of fitting into multiple niches in production systems, and relatively adapted to low-input conditions. The evolving space for these crops in production systems presents particular genetic improvement requirements that extensive gene pools are able to accommodate. Particular needs for genetic development identified in part with plant breeders relate to three areas of fundamental importance for addressing food production and human demographic trends and associated challenges, namely: facilitating integration into production systems; improving the processability of crop products; and reducing farm labour requirements. Here, we relate diverse involved target genes and crop development techniques. These techniques include transgressive methods that involve defining exemplar crop models for effective new and orphan crop improvement pathways. Research on new and orphan crops not only supports the genetic improvement of these crops, but they serve as important models for understanding crop evolutionary processes more broadly, guiding further major crop evolution. The bridging position of orphan crops between new and major crops provides unique opportunities for investigating genetic approaches for de novo domestications and major crop ‘rewildings’.

There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming communities with limited resources and data. Therefore, the objective of this study was to provide a systematic review of research on data assimilation and summarize how its application varies by country, crop, and farming systems. In addition, we highlight the implications of using process-based crop models (PBCMs) and data assimilation in small-scale farming systems. Using a strict search term, we searched the Scopus and Web of Science databases and found 497 potential publications. After screening for relevance using predefined inclusion and exclusion criteria, 123 publications were included in the final review. Our results show increasing global interest in RS data assimilation approaches; however, 81% of the studies were from countries with relatively high levels of agricultural production, technology, and innovation. There is increasing development of crop models, availability of RS data sources, and characterization of crop parameters assimilated into PBCMs. Most studies used recalibration or updating methods to mainly incorporate remotely sensed leaf area index from MODIS or Landsat into the WOrld FOod STudies (WOFOST) model to improve yield estimates for staple crops in large-scale and irrigated farming systems. However, these methods cannot compensate for the uncertainties in RS data and crop models. We concluded that further research on data assimilation using newly available high-resolution RS datasets, such as Sentinel-2, should be conducted to significantly improve simulations of rare crops and small-scale rainfed farming systems. This is critical for informing local crop management decisions to improve policy and food security assessments.

Crop models are useful tools because they can help understand many complex processes by simulating them. They are mainly designed at a specific spatial scale, the field. But with the new spatial data being made available in modern agriculture, they are being more and more applied at multiple and changing scales. These applications range from typically at broader scales, to perform regional or national studies, or at finer scales to develop modern site-specific management approaches. These new approaches to the application of crop models raise new questions concerning the evaluation of their performance, particularly for downscaled applications. This article first reviews the reasons why practitioners decide to spatialize crop models and the main methods they have used to do this, which questions the best place of the spatialization process in the modelling framework. A strong focus is then given to the evaluation of these spatialized crop models. Evaluation metrics, including the consideration of dedicated sensitivity indices are reviewed from the published studies. Using a simple example of a spatialized crop model being used to define management zones in precision viticulture, it is shown that classical model evaluation involving aspatial indices (e.g. the RMSE) is not sufficient to characterize the model performance in this context. A focus is made at the end of the review on potentialities that a complementary evaluation could bring in a precision agriculture context.

Cropping system models are deployed as valuable tools for informing agronomic decisions and advancing research. To meet this demand, early career scientists are increasingly tasked with building crop models to fit into these system modelling frameworks. Most, however, receive little to no guidance as to how to do this well. This paper is an introduction to building a crop model with a focus on how to avoid pitfalls, minimize uncertainty, and maximize value. We synthesized knowledge from experienced model builders and literature on various approaches to model building. We describe (1) what to look for in a model-building dataset, (2) how to overcome gaps in the dataset, (3) different approaches to fitting and testing the model, and (4) how to avoid common mistakes such as over-parameterization and over-fitting the model. The process behind building a crop model can be overwhelming, especially for a beginner, and so we propose a three-pronged approach: conceptualize the model, simplify the process, and fit the model for a purpose. We revisit these three macrothemes throughout the paper to instil the new model builder with the methodical mindset needed to maximize the performance and impact of their crop model.

In recent times, several studies around the globe indicate that climatic changes are likely to impact the food production and poses serious challenge to food security. In the face of climate change, agricultural systems need to adapt measures for not only increasing food supply catering to the growing population worldwide with changing dietary patterns but also to negate the negative environmental impacts on the earth. Crop simulation models are the primary tools available to assess the potential consequences of climate change on crop production and informative adaptive strategies in agriculture risk management. In consideration with the important issue, this is an attempt to provide a review on the relationship between climate change impacts and crop production. It also emphasizes the role of crop simulation models in achieving food security. Significant progress has been made in understanding the potential consequences of environment-related temperature and precipitation effect on agricultural production during the last half century. Increased CO 2 fertilization has enhanced the potential impacts of climate change, but its feasibility is still in doubt and debates among researchers. To assess the potential consequences of climate change on agriculture, different crop simulation models have been developed, to provide informative strategies to avoid risks and understand the physical and biological processes. Furthermore, they can help in crop improvement programmes by identifying appropriate future crop management practises and recognizing the traits having the greatest impact on yield. Nonetheless, climate change assessment through model is subjected to a range of uncertainties. The prediction uncertainty can be reduced by using multimodel, incorporating crop modelling with plant physiology, biochemistry and gene-based modelling. For devloping new model, there is a need to generate and compile high-quality field data for model testing. Therefore, assessment of agricultural productivity to sustain food security for generations is essential to maintain a collective knowledge and resources for preventing negative impact as well as managing crop practises.

Progress in molecular plant breeding is limited by the ability to predict plant phenotype based on its genotype, especially for complex adaptive traits. Suitably constructed crop growth and development models have the potential to bridge this predictability gap. A generic cereal crop growth and development model is outlined here. It is designed to exhibit reliable predictive skill at the crop level while also introducing sufficient physiological rigour for complex phenotypic responses to become emergent properties of the model dynamics. The approach quantifies capture and use of radiation, water, and nitrogen within a framework that predicts the realized growth of major organs based on their potential and whether the supply of carbohydrate and nitrogen can satisfy that potential. The model builds on existing approaches within the APSIM software platform. Experiments on diverse genotypes of sorghum that underpin the development and testing of the adapted crop model are detailed. Genotypes differing in height were found to differ in biomass partitioning among organs and a tall hybrid had significantly increased radiation use efficiency: a novel finding in sorghum. Introducing these genetic effects associated with plant height into the model generated emergent simulated phenotypic differences in green leaf area retention during grain filling via effects associated with nitrogen dynamics. The relevance to plant breeding of this capability in complex trait dissection and simulation is discussed.

Mechanistic and process-oriented crop models are important tools to quantify the potential impacts of climate change on crop production and yields and to evaluate the efficacy of management strategies, policies, or actions developed by the stakeholders. This review focusses on the epistemic uncertainty associated with the use of crop models. It firstly identifies the main sources of uncertainties from the perspectives of crop model inputs, methods for estimating crop parameters, crop model structure/complexity/process scale, and the underpinning experimental datasets. Pathways for managing those uncertainties are identified and future research directions are discussed. The conclusion is that strengthening experimental studies on the effects of extreme temperatures including their interaction with enhanced atmospheric CO2 concentration on crop production and further improvement, evaluation, and inter-comparison of crop models based on new experimental datasets will contribute to the reduction of uncertainties in projected climate change impacts and evaluated adaptation options. It is envisaged that crop models will continue to serve as an important research tool in addressing climate change in the agricultural sector specifically and in general with respect to global food security. Therefore, this review will provide the agroclimate impact modelling community with information on the sources of uncertainties and the ways forward to tackle this critical issue.

A crop model with genetic inputs can potentially simulate yield for a large range of genotypes, sites, and years, thereby indicating where and when a given combination of alleles confers a positive effect. We discuss to what extent current crop models, developed for predicting the effects of climate or cultivation techniques on a reference genotype, are adequate for ranking yields of a large number of genotypes in climatic scenarios with water deficit or high temperatures. We compare here the algorithms involved in 19 crop models. Marked differences exist in the representation of the combined effects of temperature and water deficit on plant development, and in the coordination of these effects with biomass production. The current literature suggests that these differences have a small impact on the yield prediction of a reference genotype because errors on the effects of different traits compensate each other. We propose that they have a larger impact if the crop model is used in a genetic context, because the model has to account for the genetic variability of studied traits. Models with explicit genetic inputs will be increasingly feasible because model parameters corresponding to each genotype can now be measured in phenotyping platforms for large plant collections. This will in turn allow prediction of parameter values from the allelic composition of genotypes. It is therefore timely to adapt crop models to this new context to simulate the allelic effects in present or future climatic scenarios with water or heat stresses.

Agricultural adaptation is crucial for sustainable farming amid global climate change. By harnessing projected climate data and using crop modeling techniques, the future trends of food production can be predicted and better adaptation strategies can be assessed. The main objective of this study is to analyze the maize yield response to future climate projections in the Guanzhong Plain, China, by employing multiple crop models and determining the effects of irrigation and planting date adaptations. Five crop models (APSIM, AquaCrop, DSSAT, EPIC, and STICS) were used to simulate maize (Zea mays L.) yield under projected climate conditions during the 2030s, 2050s, and 2070s, based on the combination of 17 General Circulation Models (GCMs) and two Representative Concentration Pathways (RCPs 6.0 and 8.5). Simulated scenarios included elevated and constant CO2 levels under current adaptation (no change from current irrigation amount, planting date, and fertilizer rate), irrigation adaptation, planting date adaptation, and irrigation-planting date adaptations. Results from both maize-producing districts showed that current adaptation practices led to a decrease in the average yield of 19%, 27%, and 33% (relative to baseline yield) during the 2030s, 2050s, and 2070s, respectively. The future yield was projected to increase by 1.1–23.2%, 1.0–22.3%, and 2–31% under irrigation, delayed planting date, and double adaptation strategies, respectively. Adaptation strategies were found effective for increasing the future average yield. We conclude that maize yield in the Guanzhong Plain can be improved under future climate change conditions if irrigation and planting adaptation strategies are used in conjunction.

Accurate production estimates, months before the harvest, are crucial for all parts of the food supply chain, from farmers to governments. While methods have been developed to use satellite data to monitor crop development and production, they typically rely on official crop statistics or ground-based data, limiting their application to the regions where they were calibrated. To address this issue, a new method called VeRsatile Crop Yield Estimator (VeRCYe) has been developed to estimate wheat yield at the pixel and field levels using satellite data and process-based crop models. The method uses the Leaf Area Index (LAI) as the linking variable between remotely sensed data and APSIM crop model simulations. In this process, the sowing dates of each field were detected (RMSE = 2.6 days) using PlanetScope imagery, with PlanetScope and Sentinel-2 data fused into a daily 3 m LAI dataset, enabling VeRCYe to overcome the traditional trade-off between satellite data that has either high temporal or high spatial resolution. The method was evaluated using 27 wheat fields across the Australian wheatbelt, covering a wide range of pedo-climatic conditions and farm management practices across three growing seasons. VeRCYe accurately estimated field-scale yield (R 2 = 0.88, RMSE = 757 kg/ha) and produced 3 m pixel size yield maps (R 2 = 0.32, RMSE = 1213 kg/ha). The method can potentially forecast the final yield (R 2 = 0.78–0.88) about 2 months before the harvest. Finally, the harvest dates of each field were detected from space (RMSE = 2.7 days), indicating when and where the estimated yield would be available to be traded in the market. VeRCYe can estimate yield without ground calibration, be applied to other crop types, and used with any remotely sensed LAI information. This model provides insights into yield variability from pixel to regional scales, enriching our understanding of agricultural productivity.