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The cultivation of pearl millet in India is experiencing important transformations. Here, we propose a new characterization of the pearl millet production environment using the latest available district level data (1998–2017), principal component analysis, and large-scale crop model simulations. Pearl millet cultivation environment can be divided in up to five environments (TPEs). The eastern part of the country (Rajasthan, Haryana, Uttar Pradesh) emerges as the only region where pearl millet cultivation has grown (+0.4 Kha/year), with important yield increase (+51 kg/ha/year), and potential surplus that are likely exported. Important reductions of pearl millet cultivated area in Gujarat (−4.5 Kha/year), Maharashtra and Karnataka (−4 Kha/year) are potentially due to economy-driven transition to other more profitable crops, such as cotton or maize. The potential rain increase could also accelerate this transition. With R2∈ [0.15–0.61], the tested crop models reflected reasonably well the pearl millet production system in the A1 (North Radjasthan) and AE1 (South Rajastan and Haryana) TPEs covering the largest area (66%) and production share (59%), especially after the use of a new strategy for environment and management parameters calibration. Those results set the base for in silico system design and optimization in future climatic scenarios.

Seedling root traits impact plant establishment under challenging environments. Pearl millet is one of the most heat and drought tolerant cereal crops that provides a vital food source across the sub-Saharan Sahel region. Pearl millet’s early root system features a single fast-growing primary root which we hypothesize is an adaptation to the Sahelian climate. Using crop modelling, we demonstrate that early drought stress is an important constraint in agrosystems in the Sahel where pearl millet was domesticated. Furthermore, we show that increased pearl millet primary root growth is correlated with increased early water stress tolerance in field conditions. Genetics including GWAS and QTL approaches identify genomic regions controlling this key root trait. Combining gene expression data, re-sequencing and re- annotation of one of these genomic regions identified a glutaredoxin-encoding genePgGRXC9as the candidate stress resilience root growth regulator. Functional characterization of its closest Arabidopsis homologAtROXY19revealed a novel role for this glutaredoxin (GRX) gene clade in regulating cell elongation. In summary, our study suggests a conserved function for GRX genes in conferring root cell elongation and enhancing resilience of pearl millet to its Sahelian environment.

We present a new algorithm for pearl millet simulation in APSIM. Compared to the actual released model, this new model increases the ability to simulate dynamic tillers by integrating recent progresses about biological understanding of the tillering mechanism. The new algorithm also offers the possibility to have an increased genetic control over key functions like canopy development and tillering through additional genotype related parameters. Next to model description, we also present the parametrization of 9444 and HHB 67-2, two genotypes broadly used in India. Overall, we could show that the new algorithm is able to reconstruct the main plant function like biomass accumulation and tillering. Some margin of improvement remains concerning the simulation of tiller cessation.

International audience; Highlights:This paper reviews ways to improve water use efficiency beyond the plant level, across time and space scales, from cells, organs, and plants, to field, farm, and landscape.Abstract:Water scarcity will be one of the main issues of the 21 st century, because of competing needs between civil, industrial, and agriculture use. While agriculture is the largest user of water, its share is bound to decrease as societies develop. Clearly, agriculture needs to become more water efficient. Improving water use efficiency (WUE) at the plant level is important although there is a long way into translating this at the farm/landscape level. As we move up from a cell/organ/plant scale to more integrated scales such as plot, field, farm system, and landscape, other factors need to be considered, including trade-offs, to possibly improve WUE. These include choices of crop variety/species, farm management, landscape design, infrastructure development, ecosystem functions, where human decisions matter. This review is a cross-disciplinary attempt to analyze ways to address WUE at these different scales where metrics of analysis are defined and trade-offs considered. The equations in this perspective paper use similar metrics across scales for an easier connection and are developed to highlight which levers, at different scales, can improve WUE. We also refer to models operating at these different scales to assess WUE. While our entry point is plants and crops, we scale up the analysis of WUE to farm systems and landscapes.

Indoor experiments with individual plants often show that transpiration rate is restricted under high vapor pressure deficit (VPD), resulting in a plateau of transpiration that increases water use efficiency (WUE) of some genotypes. We tested this hypothesis outdoors during dry or rainy seasons of India and Senegal, based on the response of the transpiration of canopy-grown sorghum plants to the reference evapotranspiration that takes both light and VPD into account. This response showed no plateau at high evaporative demand in 47 genotypes, but a large genetic variability was observed for the slope of the relationship over the whole range of evaporative demand. Unexpectedly, this slope was genetically correlated with WUE in two experiments with high evaporative demand: genotypes that most transpired had the highest WUE. Conversely, a negative correlation was observed under low evaporative demand. Genotypes with high WUE and response to evaporative demand were also those allowing maximum light penetration into the canopy. We suggest that this caused the observed high WUE of these genotypes because leaves within the canopy had sufficient light for photosynthesis whereas we observed a lower VPD in the canopy than in open air when leaf area index reached 2.5-3, thereby decreasing transpiration.HighlightsThe transpiration response to evaporative demand was genetically variable and correlated to WUE: genotypes that most transpired had highest light penetration towards leaves subjected to lower VPD than in air.

Soybean is one of the key oil crops in global food security. The objective of the current study was to determine the magnitude of soybean yield and yield gaps (Yg) in the main producing regions in Iran, the main causes and possible solutions to reduce these gaps and improve yields. This study uses an integrated approach of crop simulation and on-farm information. The SSM-iCrop2 model was used to calculate the potential yield (Yp). Furthermore, management information of soybean farms (the number of monitored farms was 224) was collected and analysed with two methods, including stepwise regression (a production model was created and based on it, the yield-limiting factors were determined) and boundary line analysis (show the optimum level of crop management and simultaneously the percentage of farms that were out of the optimal range of a specific management procedure). The results showed a Yp of 4681 kg/ha while actual yield (Ya) was around 2257 kg/ha. The main factors causing Yg of soybean in Iran were irrigation, nitrogen fertilizer, phosphorus fertilizer and sowing date. Altering soybean sowing date to late June or early July, irrigating at least five times during the growing season, applying at least 50 kg/ha nitrogen and 45 kg/ha phosphorus base application are foremost management practices that could shrink the soybean yield gap in Iran. The results presented in this study can bring relevant transferable information to other soybean production areas sharing the same latitudes and climate, and the approach can be used for other crops worldwide.

This book chapter intends to equip the readers with the basic understanding of what crop models are, answer the common questions which the crop-modelling community usually receives from the other research disciplines, and briefly describe the frequent model misuses which many times hamper broader usage of models in agriculture. We will briefly discuss the diversity of crop models and usage of the appropriate modelling tool to address the questions relevant in crop improvement programs (focus on sorghum/cereals models; APSIM). Furthermore, we will use several examples focusing on sorghum crop of how modelling approaches are currently being deployed to accelerate agricultural/cropping systems production and resilience improvement. Here, we will depict few examples of sorghum model development necessary to reflect agricultural systems in developing countries (e.g., challenges specific to model sorghum crop in Africa). We will point out to emerging directions of model development needed to address some of the global developmental goals and challenges.

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.

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 he' 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.