Can yield gap analysis be used to inform R&D prioritisation?

Global spatially explicit yield gap time trends reveal regions at risk of future crop yield stagnation

Yield gaps, here defined as the difference between actual and attainable yields, provide a framework for assessing opportunities to increase agricultural productivity. Previous global assessments, centred on a single year, were unable to identify temporal variation. Here we provide a spatially and temporally comprehensive analysis of yield gaps for ten major crops from 1975 to 2010. Yield gaps have widened steadily over most areas for the eight annual crops and remained static for sugar cane and oil palm. We developed a three-category typology to differentiate regions of 'steady growth' in actual and attainable yields, 'stalled floor' where yield is stagnated and 'ceiling pressure' where yield gaps are closing. Over 60% of maize area is experiencing 'steady growth', in contrast to ∼12% for rice. Rice and wheat have 84% and 56% of area, respectively, experiencing 'ceiling pressure'. We show that 'ceiling pressure' correlates with subsequent yield stagnation, signalling risks for multiple countries currently realizing gains from yield growth.

Inorganic fertilizer use and its association with rice yield gaps in sub-Saharan Africa

Where and which countries should receive higher priority for improving inorganic fertilizer use in rice fields in sub-Saharan Africa (SSA)? This study addressed this question by assessing the spatial variation in fertilizer use and its association with rice yield and yield gap in 24 SSA countries through a systematic literature review of peer-reviewed papers, theses, and grey literature published between 1995 and 2021. The results showed a large variation in N, P, and K fertilizer application rates and rice yield and an opportunity for narrowing the yield gap by increasing N and P rates, especially in irrigated rice systems. We identified clusters of sites/countries based on nutrient input and yield and suggested research and development strategies for improving yields and optimizing nutrient use efficiencies. Further research is essential to identify the factors causing low fertilizer use and the poor association between its use and yield in rainfed systems.

Southeast Asia must narrow down the yield gap to continue to be a major rice bowl

Southeast Asia is a major rice-producing region with a high level of internal consumption and accounting for 40% of global rice exports. Limited land resources, climate change and yield stagnation during recent years have once again raised concerns about the capacity of the region to remain as a large net exporter. Here we use a modelling approach to map rice yield gaps and assess production potential and net exports by 2040. We find that the average yield gap represents 48% of the yield potential estimate for the region, but there are substantial differences among countries. Exploitable yield gaps are relatively large in Cambodia, Myanmar, Philippines and Thailand but comparably smaller in Indonesia and Vietnam. Continuation of current yield trends will not allow Indonesia and Philippines to meet their domestic rice demand. In contrast, closing the exploitable yield gap by half would drastically reduce the need for rice imports with an aggregated annual rice surplus of 54 million tons available for export. Our study provides insights for increasing regional production on existing cropland by narrowing existing yield gaps.

Sustainable intensification for a larger global rice bowl

Future rice systems must produce more grain while minimizing the negative environmental impacts. A key question is how to orient agricultural research & development (R&D) programs at national to global scales to maximize the return on investment. Here we assess yield gap and resource-use efficiency (including water, pesticides, nitrogen, labor, energy, and associated global warming potential) across 32 rice cropping systems covering half of global rice harvested area. We show that achieving high yields and high resource-use efficiencies are not conflicting goals. Most cropping systems have room for increasing yield, resource-use efficiency, or both. In aggregate, current total rice production could be increased by 32%, and excess nitrogen almost eliminated, by focusing on a relatively small number of cropping systems with either large yield gaps or poor resource-use efficiencies. This study provides essential strategic insight on yield gap and resource-use efficiency for prioritizing national and global agricultural R&D investments to ensure adequate rice supply while minimizing negative environmental impact in coming decades.

Agronomic gain: Definition, approach, and application

Meeting future global staple crop demand requires continual productivity improvement. Many performance indicators have been proposed to track and measure the increase in productivity while minimizing environmental degradation. However, their use has lagged behind theory, and has not been uniform across crops in different geographies. The consequence is an uneven understanding of opportunities for sustainable intensification. Simple but robust key performance indicators (KPIs) are needed to standardize knowledge across crops and geographies. This paper defines a new term 'agronomic gain' based on an improvement in KPIs, including productivity, resource use efficiencies, and soil health that a specific single or combination of agronomic practices delivers under certain environmental conditions. We apply the concept of agronomic gain to the different stages of science-based agronomic innovations and provide a description of different approaches used to assess agronomic gain including yield gap assessment, meta-data analysis, on-station and on-farm studies, impact assessment, panel studies, and use of subnational and national statistics for assessing KPIs at different stages. We mainly focus on studies on rice in sub-Saharan Africa, where large yield gaps exist. Rice is one of the most important staple food crops and plays an essential role in food security in this region. Our analysis identifies major challenges in the assessment of agronomic gain, including differentiating agronomic gain from genetic gain, unreliable in-person interviews, and assessment of some KPIs at a larger scale. To overcome these challenges, we suggest to (i) conduct multi-environment trials for assessing variety × agronomic practice × environment interaction on KPIs, and (ii) develop novel approaches for assessing KPIs, through development of indirect methods using remote-sensing technology, mobile devices for systematized site characterization, and establishment of empirical relationships among KPIs or between agronomic practices and KPIs.

Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

KEY MESSAGE

Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is "How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?" Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype-Management (G-M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G-M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G-M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G-M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

Towards a global-scale soil climate mitigation strategy

Sustainable soil carbon sequestration practices need to be rapidly scaled up and implemented to contribute to climate change mitigation. We highlight that the major potential for carbon sequestration is in cropland soils, especially those with large yield gaps and/or large historic soil organic carbon losses. The implementation of soil carbon sequestration measures requires a diverse set of options, each adapted to local soil conditions and management opportunities, and accounting for site-specific trade-offs. We propose the establishment of a soil information system containing localised information on soil group, degradation status, crop yield gap, and the associated carbon-sequestration potentials, as well as the provision of incentives and policies to translate management options into region- and soil-specific practices.

Reducing soil degradation and improving soil management could make an important contribute to climate change mitigation. Here the authors discuss opportunities and challenges towards implementing a global climate mitigation strategy focused on carbon sequestration in agricultural soils, and propose a framework for guiding region- and soil-specific management options.

Reconciling yield gains in agronomic trials with returns under African smallholder conditions

Increased adoption of improved agricultural technologies is considered an essential step to address global poverty and hunger, and agronomic trials suggest intensification in developing countries could result in large yield gains. Yet the promise of new technologies does not always carry over from trials to real-life conditions, and diffusion of many technologies remains limited. We show how parcel and farmer selection, together with behavioural responses in agronomic trials, can explain why yield gain estimates from trials may differ from the yield gains of smallholders using the same inputs under real-life conditions. We provide quantitative evidence by exploiting variation in farmer selection and detailed data collection from research trials in Western Kenya on which large yield increments were observed from improved input packages for maize and soybean. After adjusting for selection, behavioural responses, and other corrections, estimates of yield gains fall to being not significantly different from zero for the input package tested on one of the crops (soybean), but remain high for the other (maize). These results suggest that testing new agricultural technologies in real-world conditions and without researcher interference early in the agricultural research and development process might help with identifying which innovations are more likely to be taken up at scale.

Large genetic yield potential and genetic yield gap estimated for wheat in Europe

Improving yield potential and closing the yield gap are important to achieve global food security. Europe is the largest wheat producer, delivering about 35% of wheat globally, but European wheat's yield potential from genetic improvements is as yet unknown. We estimated wheat 'genetic yield potential', i.e. the yield of optimal or ideal genotypes in a target environment, across major wheat growing regions in Europe by designing in silico ideotypes. These ideotypes were optimised for current climatic conditions and based on optimal physiology, constrained by available genetic variation in target traits. A 'genetic yield gap' in a location was estimated as the difference between the yield potential of the optimal ideotype compared with a current, well-adapted cultivar. A large mean genetic yield potential (11-13 t ha-1) and genetic yield gap (3.5-5.2 t ha-1) were estimated under rainfed conditions in Europe. In other words, despite intensive wheat breeding efforts, current local cultivars were found to be far from their optimum, meaning that a large genetic yield gap still exists in European wheat. Heat and drought tolerance around flowering, optimal canopy structure and phenology, improved root water uptake and reduced leaf senescence under drought were identified as key traits for improvement. Closing this unexploited genetic yield gap in Europe through crop improvements and genetic adaptations could contribute towards global food security.

Role of Modelling in International Crop Research: Overview and Some Case Studies

Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to environmental data to help make crop breeding decisions. Socio-economic applications include foresight analysis of agricultural systems under global change scenarios, and the consequences of potential food system shocks are also described. These approaches are discussed in this paper which also calls for closer collaboration among disciplines in order to better serve the crop research and development communities by providing model based recommendations ranging from policy development at the level of governmental agencies to direct crop management support for resource poor farmers.