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The potential yield of crops is not usually realised on farms creating yield gaps. Methods are needed to diagnose yield gaps and to select interventions. One method is the boundary line model in which the upper bound of a plot of yield against a potentially limiting factor is viewed as the most efficient response to that factor and anything below it has a yield gap caused by inefficiency of other factors. If many factors are studied, the cause of the yield gap can be identified (yield gap analysis, YGA). Though the boundary line is agronomically interpretable, its estimation and statistical inference are not straightforward and there is no standard method to fit it to data. We review the different methods used to fit the boundary line, their strengths and weaknesses, interpretation, factors influencing the choice of method and its impact on YGA. We searched for articles that used boundary lines for YGA, using the Boolean "Boundary*" AND "Yield gap*" in the Web of Science. Methods used to fit boundary lines include heuristic methods (visual, Binning, BOLIDES and quantile regression) and statistical methods (Makowski quantile regression, censored bivariate model and stochastic frontier analysis). In contrast to heuristic methods, which in practice require ad hoc decisions such as the quantile value in the quantile regression method, statistical methods are typically objective, repeatable and offer a consistent basis to quantify parameter uncertainty. Nonetheless, most studies utilise heuristic methods (87% of the articles reviewed) which are easier to use. The boundary line is usually interpreted in terms of the Law of the Minimum or the Law of Optimum to explain yield gaps. Although these models are useful, their interpretation holds only if the modelled upper limit represents a boundary and not just a particular realization of the upper tail of the distribution of yield. Therefore, exploratory and inferential analysis tools that inform boundary characteristics in data are required if the boundary line is to be useful for YGA. Statistical methods to fit boundary line models consistently and repeatably, with quantified uncertainty and evidence that there is a boundary limiting the observed yields, are required if boundary line methods are to be used for YGA. Practical and conceptual obstacles to the use of statistical methods are required. Bayesian methods should also be explored to extend further the capacity to interpret uncertainty of boundary line models. • The boundary line model for the yield gap can be interpreted under different agronomic hypotheses. • Boundary lines are widely-used for yield gap analysis but there is no standard method as yet. • Heuristic methods are more widely-used than statistical models, with no formal account of uncertainty. • Consistent methods for outliers, and exploratory examination of the proposed boundary are needed. • Agronomists need user-friendly software which encodes repeatable and robust boundary line methods. [ABSTRACT FROM AUTHOR]

Food security is one of the most important challenges facing human kind. A very promising approach to solve the problem is closing the yield gap, i.e. the difference between farmer’s and potential yield. A ‘complete yield gap assessment method’ must provide information regarding potential yield, actual yield and yield gap, the causes of the gap and their importance. The objective of this study was to indicate how boundary line analysis (BLA) could be applied to such an assessment. BLA was only applied to crop management practices/inputs, e.g. sowing date and rate and fertilizer applications. The data were gathered from about 700 wheat farms in Golestan province, one of the major wheat producing regions in Iran, during two growing seasons of 2013–2014 and 2014–2015. Wheat production in Golestan province can be divided into three production situations according to agro- and geo-climatology criteria: these are ‘irrigated or high-rainfall’, ‘high-yield rainfed’, and ‘low-yield rainfed’. Boundary lines were fitted to the edge of the data cloud of crop yield versus management variables using data from each of the three wheat production situations in the province. Actual farmers’ yields were 3900 kg ha –1 for irrigated, 4000 kg ha –1 for high-yield rainfed and 2000 kg ha –1 for low-yield-rainfed situations; BLA indicated that potential yields (the highest yields obtained by farmers in the sample) were 6900, 5800 and 3900 kg ha −1 for each situation, respectively. The corresponding yield gaps were high at 42%, 31% and 50%. Using BLA it was possible to determine the optimal sowing date, seeding rate, frequency and amount of nitrogen fertilizer applied, amount of nitrogen top-dressing, amount of phosphorus and potassium fertilizers and irrigation frequency. The percentage of farmers who cultivated outside of the optimal levels was also identified and was used to determine the importance of each management factor in yield gap. It was concluded that BLA as applied in the study, was a cheap and simple method which, without the need for expensive experimentation, was able to detect yield gaps and their causes in a region. The method can be used effectively in countries/regions where important yield gaps exist. [ABSTRACT FROM AUTHOR]

Chickpea is an important livelihood option and nutritious food source for many subsistence farming communities in the developing world. Although India is the biggest chickpea producing nation, the demands of its growing population are not met by domestic production. This study uses a modelling approach to quantify the region-specific constraints and yield gaps limiting chickpea productivity and evaluates the potential for boosting production in the major chickpea growing regions of India. Information on bio-geo-physical properties (weather, soil, crop, management) of these regions was collated and the SSM-iLegume model used to reproduce seasonal variability and potential yield for the major chickpea producing districts to estimate the yield gap. Further, we estimated the difference between the yield potential and the currently achieved yields; i.e. yield-gap. The results showed that India has the capacity to produce 40% more chickpea (i.e. 80% of the achievable yield) than is the current production status under the standard crop management practices. We also found that chickpea crop production in rain-fed systems is largely limited by water availability during the season (∼64%) but with large variability in the drought stress effect on yields between the investigated districts. Observed geo-bio-physical properties of the districts and simulation results of yield gap analysis were used to cluster chickpea-growing districts into six distinct units with higher degrees of similarities; i.e. homogeneous system units (HSU). Within each HSU a similar system response to genotype-by-management (GxM) intervention is expected and the effects of particular interventions could be further tested using the modelling set-up developed for this study. The identified HSUs, each with a well-defined set of yield-limiting constraints, are proposed as authentic breeding units in crop improvement programs (“target population of environments”) and we further discuss the need to use the HSU-specific breeding strategy to enhance chickpea production in India. [ABSTRACT FROM AUTHOR]

This position paper honours agricultural scientist and colleague, Professor Bob Loomis, by discussing the urgent global challenge of food security and the related impacts on the environment facing agricultural science and society in the next critical 20 years. It uses the concepts of potential and actual (farm) crop yields and the yield gap between them to assess current and future opportunities for food supply to satisfy increasing demand. The cropping world is seen in two parts. The first part predominantly comprises low-input farming with very large yield gaps and a faster growing demand that can only be met with increasing imports. For these regions, a well-established strategy is outlined for crop intensification through yield-gap closure that is essential for reducing rural malnutrition and poverty, and curtailing the likelihood of high food prices. For success, it must be complemented with strategies to remove the serious institutional and infrastructural barriers faced by farmers. The second part has more or less intensified, and yield gaps are generally small to moderate: it will fairly comfortably meet the demand from population growth. For these regions, some further yield gap closure is still possible but more importantly greater potential yields are required although the chances of accelerating this are discussed and seen to be limited. For all regions, sustainable intensification of cropping, predominantly on existing arable lands, is the best way forward. Combining sustainability with intensification is not a contradiction and is, in fact, essential; sustainability requires the efficient use of all inputs in cropping, and husbandry of the soil and agricultural biodiversity needed to continue to raise productivity. Off-farm environmental impacts are inevitable, but not insurmountable, hurdles. All aspects of sustainability require boosted RD&E and sound rural policies. Greater management skills for farmers and all others involved in crop production are also essential. Contestation based on biophysical aspects of food production and its impacts can be resolved through effective research and development with farmers, while that based on Northern cultural and normative views must not be allowed to obscure the goal of affordable food for all, and reward for farmers comparable with the rest of their societies. [ABSTRACT FROM AUTHOR]

Rice-rapeseed cropping system (hereafter as rice-rapeseed system), as a major cropping system in the Yangtze River Valley (YRV) in China, plays an important role in national food and edible oil security. However, the production capacity of this system remains largely unknown. We evaluated the yield gap (Yg), energy gap (Eg), and production gap (Pg) of rice-rapeseed system. Regions with largest exploitable Pg were determined and possible scenarios were further analyzed for the evaluation of self-sufficiency rate (SSR) in the next decade. This study followed Global Yield Gap Atlas (GYGA) protocol (www.yieldgap.org) according to a bottom-up upscaling method with well-calibrated crop models to evaluate Yg, Eg, and Pg from reference weather stations to regions and national levels based on detailed database of daily weather, soil, crop management practices, and high-yielding experiments. Possible scenarios for the assessment of SSR were conducted further for rapeseed in the system. The relative Eg of the system of 42% was mainly affected by the larger relative Yg of 50% in rapeseed rather than that of 37% in rice. By Yg closure to 80% of Yp, a Pg was 8.54 million tons (Mt) for rice and 5.31 Mt for rapeseed in the system. Among the major planting provinces, Anhui had the largest Pg in rice, and Hunan had the largest Pg in rapeseed. As SSR in rice is high in China, we further evaluated rapeseed SSR in the system. We found that SSR in rapeseed was 79% currently and would decrease to 54% in 2031. According to scenario analysis, utilization of 4–39% of current winter fallow fields in YRV was important besides Yg closure for the system to reach a feasible SSR of 80% in the next decade. Large room existed in rice-rapeseed system for further yield increase, especially for rapeseed in the system. Partially utilization of winter fallow fields in YRV is promising to ensure system SSR in the next decade. This study has evaluated the Yg, Eg, and Pg for rice-rapeseed system, located the regions with large production capacity, and explored feasible strategies to increase rapeseed production and ensure high-level SSR in YRV. • Relative energy gap is 42% in rice-rapeseed system. • Relative yield gap of rice and rapeseed are 37% and 50% in the system, respectively. • Anhui and Hunan are the regions with largest production gap. • Utilization of 4–39% of winter fallow fields is important to ensure self-sufficiency. [ABSTRACT FROM AUTHOR]

A science-based, reliable, and feasible fertilizer recommendation method is required to respond to the low nutrient use efficiency caused by inappropriate fertilization practices. Soil test-based fertilizer recommendations are difficult to use for smallholder farms because of constraints such as access, cost and timeliness in multiple cropping systems. In this study, we combined on-farm experiments from 2012 to 2014 in 20 farmers’ fields on spring maize in Northeast China with a simulation model (Hybrid Maize model), to test the continual performance in agronomic, economic and environmental aspects of the Nutrient Expert for Hybrid Maize decision support system. Six treatments were set as follows: Nutrient Expert (NE), farmers’ practice (FP), soil testing (OPTS) and nitrogen (N), phosphorus (P), and potassium (K) omission treatments based on NE. We estimated yield gaps as the difference between simulated yields with the Hybrid Maize model and measured yields; calculated economic benefit and nutrient use efficiency; and estimated greenhouse gas emissions using published equations approximating nitrous oxide emissions as a function of N fertilizer rate. On average, the NE, FP, and OPTS treatments attained yields of 80%, 74%, and 77% of the potential yield, respectively. The exploitable yield gap between the NE and FP treatments was 0.9 t ha −1 , and between the NE and OPTS treatments was 0.5 t ha −1 . On average, the NE treatment increased the gross return above fertilizer cost (GRF) by US$303 and US$167 compared with the FP and OPTS treatments across all sites, respectively, in which about 91% and 98% of increase GRF was attributed increase in grain yield rather than reduction in fertilizer cost. There were slightly higher nutrient use efficiencies under the NE treatment than under the OPTS treatment. Relative to the FP treatment, however, on average, the NE treatment increased recovery efficiency of N, P, and K by percentage point of 12, 15, and 10, respectively. Agronomic efficiency of N, P, and K were increased by 6, 35, and 10 kg kg −1 , respectively. Finally, partial factor productivity increased by 14 kg kg −1 for N and 45 kg kg −1 for P while decreased by 29 kg kg −1 for K. Furthermore, the calculated soil inorganic N at harvest of maize crop, total greenhouse gas (GHG, kg CO 2 eq ha −1 ) emissions, and GHG emission intensity (kg CO 2 eq t −1 grain) were 42%, 17%, and 23% lower in the NE treatment than the FP treatment, respectively. We conclude that the Nutrient Expert for Hybrid Maize system has the potential to close existing yield gaps in the spring maize production systems of Northeast China by improving yield, nutrient use efficiency, and profitability with low environmental pollution. [ABSTRACT FROM AUTHOR]

Iran is one of the major chickpea ( Cicer arietinum L.) producing countries of the world. Average chickpea yield in Iran is about 500 kg ha −1 while the world average is 900 kg ha −1 . The objective of this study was to investigate chickpea water limited potential yield ( Y w ) and yield gap in Iran. The analysis was based on data from five representing chickpea producing locations of Iran. Estimated country Y w and yield gap were 991 and 463 kg ha −1 , respectively, indicating that farmers have reached 53% (range: 38–64%) of Y w . If farmers could reach 80% of Y w of their locations, by improving agronomy practice, country average yield would increase by 50%, from 528 to 793 kg ha −1 . A key finding of the study was that chickpea yield in Iran is largely limited by inefficient use of environmental resources and not the genetics of the current cultivars. Much higher yield increase is obtainable via an agronomic option than a genetic improvement option: using shorter duration cultivars increased Y w to 1237 kg ha −1 (25% increase), but applying a single irrigation of 60 mm at first-pod alone or in combination with shorter duration cultivars increased Y w to 1804 kg ha −1 (85% increase) and to 1997 kg ha −1 (104% increase), respectively. Thus, tripling chickpea production would be feasible using a single irrigation with or without shorter duration cultivars (from 528 to 1443 or 1598 kg ha −1 ). The availability of water for the single irrigation is discussed. [ABSTRACT FROM AUTHOR]

Given the importance of crop yield and yield progress, this review endeavours to clearly define the different representations of yield, discuss their measurement, and elucidate some controlling factors in yield change. For a field, farm, district or region, average farm or actual yield (FY) is central, but potential (and water-limited potential) yield (PY, PY w ) is also an important yardstick. PY is defined here as the measured yield of the best cultivar, grown with optimal agronomy and without manageable biotic and abiotic stresses, under natural resource and cropping system conditions representative of the target area. Economic yield, governed by considerations of profit and risk, and record and theoretical yield, complete the picture. Yield gap is defined as the difference between PY and FY under the same environment. Across most crop-region combinations in the last 2 to 3 decades, FY progress has been associated with both PY progress and yield gap closing, and a simple model, based on linear regression against time, is proposed for understanding this. PY advance is the result of plant breeding and new agronomy (and their interaction, usually positive), while yield gap closing arises with the adoption by farmers of known innovations faster than new ones are invented. Unravelling the true technological component in apparent progress in PY, and especially in FY, is not necessarily simple, and confounding factors are listed and discussed. [ABSTRACT FROM AUTHOR]

The potential yield of potato is defined as the theoretical yield that can be assessed for a well-adapted cultivar, grown from the best possible seed under optimal conditions. More than in crops that are grown from generative seeds, the growth, development, yield and quality of the potato crop are strongly influenced by the quality of the seed tubers, including their genetic, physical and physiological quality and their seed health status. Potato is very variable in maturity type: late cultivars can intercept large quantities of light whereas early cultivars show more efficient resource use. We describe potential and actual yields as well as impact of climate change on these yields, based on simple, robust models. Potential yields may be as high as 160 Mg ha −1 , in production systems with abundant irrigation, high radiation levels and long seasons. In such systems, actual yields of above 120 Mg ha −1 are feasible. However, potato is also grown as a short-cycle crop and in those conditions potential and actual yields are much lower. Potential yields might not change much in potato over time, but there is still a large gap between actual and potential yields to fill. The ratio between actual and potential yield ranges from 10 to 75%, but typical values are between 30 and 40%, although they obviously depend on the level of input. These ratios allow great yield improvements provided inputs are economically feasible in practice and if climate change does not interfere. In most countries, yields of potato might increase by climate change, provided water supply remains adequate. Main changes through climate change will probably occur by changes in the number of growing days per crop cycle. In some areas with abundant potato production, such as the Indo-Gangetic plains, however, climate change might reduce yield because of a reduction in number of growing days, but it has been projected that the effects of climate change on potato will be regionally diverse. [ABSTRACT FROM AUTHOR]

The largest reported soybean grain yield is approximately three-fold more than the highest reported U.S. average yield. An understanding of yield determination is needed to identify avenues for increasing yield and for defining the yield potential of soybean. To illustrate physiological traits important for yield determination, we used a framework that models yield as the product of seed number (seed m −2 ) and individual seed mass (mass seed ). Developmentally, seed m −2 is determined first and is proportional to the biomass accumulation rate (BAR, g m −2 d −1 ) and the fraction of assimilate allocated to reproductive structures. Seed m −2 is inversely proportional to the individual seed growth rate (ISGR, mg seed −1 d −1 ) where the ISGR represents the minimum amount of assimilate necessary to prevent a flower or pod from aborting. Hence, seed m −2 can be increased by optimizing conditions for crop growth (e.g., radiation interception, stress-free environment, high soil fertility levels) and having a low ISGR. Determination of mass seed occurs later during ontogeny than seed m −2 and can be expressed as the product of the ISGR and the effective seedfilling period (EFP, d). Variation among genotypes for ISGR is quite large and is generally not affected greatly by the environment. There is also genotypic variation in the EFP, but the EFP is decreased by a variety of biotic and abiotic stresses. Our analysis indicates that reaching the potential yield of soybean depends upon high BAR and extending the EFP, and a key factor affecting both of these variables is ensuring non-limiting crop nutrition, especially nitrogen. Strategies for increasing soybean maximum yield include early planting (which extends the EFP), optimizing crop nutrition, minimizing biotic and abiotic stresses, and developing breeding programs tailored for high yield environments. Characterizing physiological traits important for yield with genetic markers offers tools for combining favorable traits for high-yield environments. [ABSTRACT FROM AUTHOR]

Highlights: [•] We simulated the effects of drought and nutrient stress on rainfed lowland rice. [•] Yield reduction due to drought was smaller than that due to nutrient stress. [•] Fertilizer rates to achieve a particular yield target were estimated. [•] We recommend site-specific fertilizer application for rainfed lowland rice. [ABSTRACT FROM AUTHOR]

Highlights: [•] Genotypes selected for higher yield under intermittent drought experiments were well adapted to farmers’ fields. [•] Selection for high yield potential and drought tolerance in on-station managed water conditions enabled the identification of promising lines for the rainfed lowlands. [•] High positions generally produced low yield but no significant interaction between position and genotypes existed, therefore usefulness is limited if number of sites is limited. [Copyright &y& Elsevier]

Highlights: [•] Intermittent drought screening was compared with terminal drought screening. [•] Provided drought level is appropriate, intermittent drought can be utilized for drought screening. [•] Advantage of intermittent drought screening in wet season is only one field condition required. [•] We identified 6 genotypes adapted to drought-prone rainfed lowlands. [•] Genotypes adapted to drought had high drought response index and high potential yield. [ABSTRACT FROM AUTHOR]

Continued global wheat yield increase (about 1.3% p.a. for 2000–2019) remains an essential condition for greater world food security. Relevant to this challenge is the rise in average farm yield (FY) of irrigated spring wheat in the Yaqui Valley of northwest Mexico from 2 to 7 t/ha between 1960 and 2019. Since the early 1950s the region has been the prime target of wheat research by the International Maize and Wheat Improvement Centre (CIMMYT) and its predecessors, research still having significant impact on wheat in the developing world, a grouping that today delivers more than half the world's wheat. FY increase was investigated in detail by dividing the interval into three 20-year periods, correcting FY for the strong influence of inter-annual variation in January to March minimum temperature (Tmin J-M, warming lowering yield around 7%/°C) and measuring the remaining linear increase in FY (Fischer et al., 2022). Total yield increase, corrected for Tmin J-M and CO 2 rise, relative to average yield in each period, was 4.17%, 0.47%, and 1.59% p.a. for 1960–79, 1980–99, and 2000–19, respectively. The breeding component, estimated by the increase in the Varietal Yield Index in farmers' fields, rose at 0.97%, 0.49%, and 0.71% p.a., respectively. The remaining yield change (3.16, −0.02% and 0.87% p.a., respectively) comprised the net effect of improved crop management (agronomic progress) plus that of off-farm changes, together here called agronomy+. Major changes in agronomy included: a large increase in fertiliser N use, benefitting early on from a large positive variety × N interaction; in the second period a switch to planting on raised beds and a decline in rotational diversity; and in the final period, consolidation of operational crop units and probably more skilful and timely management. Off-farm developments saw strong government financial support in the first period, but in the second period breakdown of the traditional small holder land system and withdrawal of government support. The last period saw better prices and improved access to technical advice. Breeding progress is expected to continue in the Yaqui Valley but at a slowly diminishing rate (currently 0.66% p.a.), while progress from new agronomy appears limited. Although FY gaps are small, some gap closing remains possible, and 1.2% p.a. FY progress is estimated for the next 20 years in the absence of new technologies. World wheat food security without area increase will increasingly depend on developing countries where yield gaps are generally wider and gap closing prospects better. Biophysical sustainability of the Yaqui Valley wheat system is moderately good but N management and diversity can be improved. • Farm yield (FY) rose 250%, linear gain (% p.a.) was 4.17 (60-79), 0.47 (80-99) and 1.59 (2000-19). • Breeding contributed 0.97, 0.49 and 0.71 % p.a., respectively, to this FY progress. • Thus agronomy and off-farm factors contributed 3.16, -0.02 and 0.87 % p.a. respectively. • Increased N fertiliser was the major agronomic positive; policy changes 1980-99 were negative. • Predicted FY growth is 1.2% p.a.; system sustainability is good but N management and diversity lag. [ABSTRACT FROM AUTHOR]

There is a strong need for quantitative advice on fertiliser rates for annual crops. There is also need for a method to estimate the degree to which nutrient availability is limiting yields in a wide variety of field experiments. Such information would greatly increase the confidence with which generalisations can be made from experimental results.Detailed “mechanistic” simulation models are often ill-suited for calculations that span a wide range of field conditions. Such models may need large amounts of site-specific soil and crop details, and can be difficult to validate at the level at which they ostensibly simulate the processes involved. An alternative approach is to use semi-mechanistic or empirical models that predict yield on the basis of response curves for fertiliser applications and soil nutrient content at planting. While these can be very reliable within their calibration range, they generally lack the ability to cope with changes in factors such as the weather, plant population or cultivar.This paper derives PARJIB, a model that: (1) retains the functional simplicity and modest data requirements of response curve-style empirical models; (2) provides a meaningful framework for interpreting field experiments; (3) can be used in a very wide range of conditions. In PARJIB, yield response to nutrient supply is dictated by the maximum yield, which is the potential yield (driven by climate and cultivar characteristics) adjusted for plant population and water stress. Emphasis is placed on scaling both the nutrient supply and the simulated crop yield relative to the maximum yield. Interactions between the effects of different nutrients are simulated using a simple equation that combines the effects of nutrients in less than optimal supply. The model can be calibrated against field measurements of crop performance. This is readily achieved using a genetic algorithm technique, although other non-linear fitting techniques may be used. Calibration requires information on maximum or potential yield (usually calculated from daily air temperature, rainfall/irrigation, and radiation), fertiliser and irrigation applications, potential evapotranspiration, and soil information (estimates of available water capacity and soil chemical analyses such as Olsen P, exchangeable K, etc.). Provided they are consistent within a calibration dataset, a wide range of techniques may be used for the soil chemical analysis, although the calibration produced is specific to the actual techniques used. Performance of the model for maize (Zea mays L.) is described in the second paper of this series. [Copyright &y& Elsevier]

This paper tests PARJIB, a simple but powerful model to analyse and forecast yield responses to nutrients in annual crops. The model was calibrated for maize using data obtained under a very wide range of conditions in New Zealand from 1996 to 1999. The calibration process used a genetic algorithm technique. After a preliminary calibration using about half of the available data and only one cultivar, the model performed well, accounting for 66–73% of the observed yield variation in independent datasets that included five cultivars. When simulating the test datasets, the largest errors were associated with one long-season cultivar. The model was then recalibrated against all available data. That dataset spanned five cultivars, four regions of New Zealand, a wide range of soil and weather conditions, and a range of fertiliser application treatments. Grain yields varied from 4 to 18 t/ha. The root mean square (RMS) error of calibration was 0.92 t/ha (9.3%), and the model accounted for 83% of the observed variation in yield. We checked various model predictions against previously published results, and found good agreement. The model indicates strong interactions between water stress, plant population and the need for nutrients. The PARJIB model has substantial potential to aid growers in fertiliser decisions, and to assist researchers in the analysis and interpretation of field experiments. [Copyright &y& Elsevier]

This in-depth analysis of the rise in farm yield (FY) of irrigated wheat in the Yaqui Valley of north-west Mexico, from around 2 to 7 t/ha between 1960 and 2019, begins by highlighting a weather component overlooked in modelling and other thinking about wheat and climate change, namely the dominant role of natural annual variation in minimum temperature (Tmin). Year to year fluctuations around the increasing yield trend were substantial (st.dev. 591 kg/ha) and were highly correlated negatively with annual fluctuations in the average Tmin January to March (Tmin J-M) which ranged from 5.0 to 11.5 oC in this period lasting from mid tillering to early-mid grain fill. Dividing the 60-year period into three consecutive 20-year periods and using multiple linear regression improved the accuracy of estimates of FY as a linear function of time in each period (responding inter alia to better technology including breeding and agronomy), with slopes of 4.5%, 0.6%, and 1.8% p.a. for 1960–79, 1980–99, and 2000–19, respectively. Tmin J-M coefficients were −218, −413, −406 kg/ha/oC, or −5.9, −8.1, −6.6%/oC, respectively, with R2 always close to 0.9. Combining results, the FY response to Tmin J-M was close to −7%/oC, such that over the 60-year period the Tmin J-M increase of 1.0 oC contributed a 7% reduction in FY at constant CO2. The improved estimate of FY slopes with respect to time revealed novel variation across the 60 year period to be dissected in a following paper. Annual fluctuations in Tmax J-M were not correlated with variation in Tmin J-M nor with variation in FY. Crop simulation modelling of potential yield (PY) corroborated the negative effect of increased Tmin J-M on yield and suggested that increased Tmin J-M primarily decreased days to anthesis, biomass and number of grains (/m2). Measurements retrieved from long term well-managed experiments in the Valley under constant agronomy and cultivars confirmed all the above predicted responses to Tmin J-M. Our results align with the few published studies increasing crop night temperature in the critical period of around 30 days up to the end of anthesis when grains/m2, and hence yield, was inversely related to rate of development. In the Yaqui Valley this was strongly associated with Tmin variation. This phenomenon, and the roles of Tmax and solar radiation (Rs) variation, are discussed in detail. • Average farm yield (FY) in the Yaqui Valley rose from 2 t to 7 t/ha but annually fluctuated widely. • FY fluctuations responded negatively to Tmin Jan to Mar with a slope of about ̶ 7% /0C, but were unrelated to Tmax. • Correcting for Tmin fluctuations improves the estimate of annual FY advance (technology). • Crop modelling and experiments identify development rate and grains/m2 key responders to Tmin. • Results are relevant to correctly assessing technological progress and adapting wheat to chronic warming. [ABSTRACT FROM AUTHOR]

• Outstanding yields by cane varieties were obtained under unlimited conditions in large field experiments in tropical Brazil. • Guadalupe, PI State, is a more favourable production environment for sugarcane than São Romão, MG State. • The radiation use efficiency was similar between varieties up to 6–8 months regardless of site. • The radiation use efficiency declined differently among varieties after the onset of the growth slowdown. • Traits such as stalk fraction and growth slowdown with ageing were derived for use in modelling with the APSIM-Sugar model. Aiming to gain an understanding of how the genotype × environment × management (G×E×M) interaction influences the yield accumulation by elite sugarcane varieties in Brazil, a large dataset from field plot experiments carried out in two tropical sites (Guadalupe, 6.8 °S; São Romão, 16.4 °S) involving distinct planting dates, varieties and harvest ages, was analysed with statistical techniques and with the APSIM-Sugar model. Radiation use efficiency (RUE) was determined via a series of regressions and employed in an analysis of variance to investigate site, seasonal, developmental, and varietal differences. Outstanding yields were achieved at both sites. RUE declined as the crop progressed, confirming previous observations on declining RUE with age, known as the reduced growth phenomenon (RGP). RUE was always greater at Guadalupe than São Romão, evidencing that Guadalupe is a more suitable environment for sugarcane production, favoured by higher air temperatures during crop establishment and canopy formation. Varietal differences in RUE appeared only after the early developmental stage, and the observed growth slowdown with age was consistent across the two experimental sites, indicating that RGP is a varietal trait that should be considered for high-yielding environments. The process-based APSIM-Sugar model was set up with recently determined canopy traits and a new RGP feature based on leaf appearance. RGP parameters were obtained for each variety and site through calibration. The calibrated model was accurate to account for yield accumulation by the varieties in both experiments. The new parameters were evaluated with independent datasets from other local experiments at each tropical site as well as from published rainfed experiments in sub-tropical Southeast Brazil. Independent verification of the RGP traits added confidence in the new way of dealing with RGP based on leaf stage. The G×E×M interaction on yield accumulation can now be explored more confidently with APSIM-Sugar for the purpose of optimising the choice of varieties, planting dates and harvest ages for sugarcane industries in favourable irrigated lands in tropical Brazil. [ABSTRACT FROM AUTHOR]

• Genotype by Environment by Management (GxExM) interactions hampers crop improvement. • Crop simulation revealed large/variable yield gaps (∼70 %). • Filling 80 % of yield gap would increase production by 8 M tons. • Modelling output and biophysical traits were clustered (HPU). • Grouping by HPU reduced GxExM interactions and will help refine breeding targets. The high degree of Genotype by Environment by Management (GxExM) interactions is a serious challenge for production and crop improvement efforts. This challenge is especially true for a crop like groundnut that is often grown as a rainfed crop in diverse environments and management, leading to considerable production fluctuations among regions and seasons. Developing a means to characterize the drivers of variable yield and to identify region specific breeding objectives were the main motivations for this research, using groundnut production in India, as a case study for rainfed crops. Historically, five groundnut production areas have been considered by Indian crop improvement programs. Our objectives were to assess the relevance of this zonation system and possibly to re-define production areas with a higher degree of similarities into homogeneous production units (HPUs). Towards this, we used yield gap analysis and the geo-biophysical characters of the production region to understand and deal with GxExM interactions. Weather and soil data, crop parameters, and management information data were collected and groundnut production was simulated at the district scale over 30 consecutive years. Consequently, the geographic distribution of the potential yields and the yield gaps were first estimated to understand the main production limitations in a given region. Large and variable yield gaps (with a mean of ∼70 %) were observed and results revealed a readily exploitable production gap (∼ 8 M tons), which might be bridged by following recommended agronomic practices. Water deficit limited the yield potential by an average of 40 %, although with large variability among districts. However, large and variable yield gaps remained. To resolve the unexplained variation, principal component and cluster analysis of agronomic model output together with geo-biophysical indicators for each district were carried out. This resulted in seven HPUs, having well-defined production-limiting constraints. Grouping by HPU greatly reduced variance in actual and simulated yields, as compared to grouping across all groundnut production zones in India. The HPU based approach delimited precise geographic regions within which HPU-specific GxM products could be designed by crop improvement programs to boost productivity. [ABSTRACT FROM AUTHOR]

• We studied the dynamic of maize crop-growth-rate during the post-flowering period. • Potential crop-growth-rate was estimated by two robust and independent methods. • The gap in current vs. attainable post-flowering dry-matter is at least 18.2%–47.8%. • These results may represent a benchmark for improvements in maize potential yield. • We discuss possible reasons of this gap and some challenges to reduce it. After canopy closure and in the absence of limitations by water or nutrient availability, crop growth rate (CGR) of maize (Zea mays L.) is ultimately constrained by the daily incident radiation and temperature of the environment. Sustaining maximal canopy photosynthetic capacity after-flowering is, then, a necessary but not a sufficient condition to increase maize dry-matter production. The aim of the present study was to determine the extent of the photo-thermal environment limitation to CGR during the post-flowering period in current maize crops. Dynamic of CGR was studied in two well-irrigated and nourished maize field experiments (Exp. 1 and Exp. 2 for 2010−11 and 2011−12 cropping seasons, respectively) on conventional crops (i.e. full-season hybrid planted early in the season) at Balcarce, Argentina (37° 45' S, 58° 18' W; 130 m a.s.l.). Two independent methods were performed to benchmark the CGR of these conventional crops during the post-flowering period: i) empirical CGR values obtained under the same weather conditions from younger maize crops, and ii) theoretically estimated potential CGR, obtained as a function of daily incident radiation and potential radiation use-efficiency (RUE). Conventional crops reached the maximal CGR near flowering in mid-January, being 51.2 g m−2 d−1 and 58.8 g m−2 d−1 in Exp. 1 and Exp. 2, respectively. Afterwards, CGR decreased progressively towards crops maturity late in March. Estimates, from either the empirical or the theoretical method, indicated that although attainable-CGR decreases progressively towards the end of the cropping season, it sustains higher values than those achieved by conventional crops after flowering. Differences in attainable vs. actual-CGR was almost exclusively attributable to RUE, which, in turn, could not be explained solely by the post-flowering foliar nitrogen withdrawal. Differences between actual (1987 g m−2 in Exp. 1 and 1614 g m−2 in Exp. 2) and potential post-flowering dry-matter production defined gaps that were in the range 18.2%–47.8%. From these results, it can be concluded that the photo-thermal environment is not, at least so far, the limiting factor to the post-flowering maize growth. Further research is needed, however, to analyze the viability of increasing potential yield of maize through the closure of these estimated gaps. [ABSTRACT FROM AUTHOR]