Prediction of potential and attainable yield of wheat : A case study on yield gap

This study reports the role of field experimentation and system simulation in better quantifying the productivity of wheat crop, and examine how knowledge on potential productivity can improve the efficiency of the production system. When knowledge from field experimentation is utilised into crop weather simulation models, gap between actual, attainable and potential yield for a given environment can be determined and opportunities for yield improvement can be assessed. Results show that while actual district average yields show increasing trend, decreasing trend is noticed in potential and attainable yield. While the total and management yield gap is decreasing over time, research yield gap does not show any trend, it is nearly stagnant from early eighties to late nineties. The study reported here presents the advantage of simulation models to determine the yield gap against a variable annual yield potential for a agro-climatic region.

Simulating Regional Cassava Yield Gap in Nigeria

Cassava production is essential for food security in Sub-Saharan Africa and serves as a major calorie- intake source in Nigeria. Here we use a crop model, LINTUL5, embedded into a modeling framework SIMPLACE to estimate potential cassava yield gaps (Yg) in 30 states of Nigeria. Our study of climate parameter influence on the variability of current and potential yields and Yg shows that cumulative radiation and precipitation were the most significant factors associated with cassava yield variability (p = 0.01). The cumulative Yg mean was estimated as 18202 kg∙ha-1, with a maximum of 31207 kg ha-1 in Kano state. Across the states, nutrient limitation accounts for 55.3% of the total cassava yield gap, while the remaining 44.7% is attributed to water limitation. The highest untapped water-limited yields were estimated in States, such as Bauchi, Gombe, and Sokoto, characterized by the short rainy season. Conclusively, the current cassava yield levels can be increased by a factor of five through soil fertility enhancement and with irrigation, particularly in semi-arid regions.

Importância dos modelos de simulação de culturas diante os impactos das alterações climáticas sobre a produção agrícola - Revisão

O efeito climático é o principal responsável pelas oscilações no rendimento produtivo. Logo, é esperado que as mudanças do clima promovam alterações na agricultura, comprometam a sustentabilidade e a segurança alimentar, especialmente, em áreas semiáridas. O entendimento da amplitude desses fatores e suas consequências no rendimento agrícola mediante os diferentes cenários climáticos, regionais e tecnológicos são fundamentais nas tomadas de decisões. Para as análises desses diversos cenários, os modelos de simulação de culturas se caracterizam como ferramentas funcionais e com eficientes performances na estimativa dos níveis de produtividades, desde que devidamente calibrados e validados com dados consistentes e representativos. Dentre os modelos de simulação podemos destacar: AquaCrop - FAO, ZAE - FAO, CROPGRO e Apsim como aqueles de maiores aplicabilidades nas culturas agrícolas, sendo utilizados de maneira recorrente em diversos estudos para fins do conhecimento das lacunas de produtividade agrícola, ou “Yield Gap”. Esta revisão analisou os impactos das alterações climáticas na agricultura e o levantamento de informações dos principais modelos de simulação de culturas. Mediante síntese das informações levantadas, pode-se evidenciar o eminente impacto das alterações climáticas sobre o cenário agrícola futuro, proporcionando maior vulnerabilidade agrícola. Logo, destaca-se a importância do uso de modelos de simulação de culturas para conhecimento das lacunas de produtividade e potencial produtivo. Contudo, é evidente a necessidade de pesquisas mais detalhadas sobre a aplicabilidade dos modelos em cenários agrícolas diversos e situações climáticas amplas.Palavras-chave: modelos de simulação; sazonalidade climática; práticas resilientes; “yield gap”. Importance of crop simulation models in view of the impacts of climate change on agricultural production – Review A B S T R A C TThe climatic effect is the main responsible for the fluctuations in the productive yield. Therefore, it is expected that climate change will promote changes in agriculture, compromise sustainability and food security, especially in semi-arid areas. Understanding the breadth of these factors and their consequences on agricultural income through different climatic, regional and technological scenarios are fundamental in decision-making. For the analysis of these different scenarios, the crop simulation models are characterized as functional tools and with efficient performances in the estimation of the productivity levels, as long as they are properly calibrated and validated with consistent and representative data. Among the simulation models we can highlight: AquaCrop - FAO, ZAE - FAO, CROPGRO and Apsim as those with the greatest applicability in agricultural crops, being used in a recurring manner in several studies for the purpose of understanding agricultural productivity gaps, or “Yield Gap”. This review analyzed the impacts of climate change on agriculture and the gathering of information on the main crop simulation models. By synthesizing the information collected, it is possible to highlight the imminent impact of climate change on the future agricultural scenario, providing greater agricultural vulnerability. Therefore, the importance of using crop simulation models to understand the gaps in productivity and productive potential is highlighted. However, there is a clear need for more detailed research on the applicability of models in diverse agricultural scenarios and broad climatic situations.Keywords: simulation models; climatic seasonality; resilient practices; yield gap.

The Analysis of Wheat Yield Variability Based on Experimental Data from 2008–2018 to Understand the Yield Gap

Among European countries, Poland has the largest gap in the grain yield of winter wheat, and thus the greatest potential to reduce this yield gap. This paper aims to recognize the main reasons for winter wheat yield variability and shed the light on possible reasons for this gap. We used long-term datasets (2008–2018) from individual commercial farms obtained by the Laboratory of Economics of Seed and Plant Breeding of Plant Breeding and Acclimatization Institute (IHAR)-National Research Institute (Poland) and the experimental fields with high, close to potential yield, in the Polish Post-Registration Variety Testing System in multi-environmental trials. We took into account environment, management and genetic variables. Environment was considered through soil class representing soil fertility. For the crop management, the rates of mineral fertilization, the use of pesticides and the type of pre-crop were considered. Genotype was represented by the independent variable year of cultivar registration or year of starting its cultivation in Poland. The analysis was performed using the CART (Classification and Regression Trees). The winter wheat yield variability was mostly dependent on the amount of nitrogen fertilization applied, soil quality, and type of pre-crop. Genetic variable was also important, which means that plant breeding has successfully increased genetic yield potential especially during the last several years. In general, changes to management practices are needed to lower the variability of winter wheat yield and possibly to close the yield gap in Poland.

Managing Density Stress to Close the Maize Yield Gap

Continued yield increases of maize (Zea mays L.) will require higher planting populations, and enhancement of other agronomic inputs could alleviate density-induced stress. Row spacing, plant population, P-S-Zn fertility, K-B fertility, N fertility, and foliar protection were evaluated for their individual and cumulative impacts on the productivity of maize in a maize-soybean [Glycine max (L.) Merr.] rotation. An incomplete factorial design with these agronomic factors in both 0.76 and 0.51 m row widths was implemented for 13 trials in Illinois, United States, from 2014 to 2018. The agronomic treatments were compared to two controls: enhanced and standard, comprising all the factors applied at the enhanced or standard level, respectively. The 0.51 m enhanced management control yielded 3.3 Mg ha–1 (1.8–4.6 Mg ha–1 across the environments) more grain (25%) than the 0.76 m standard management control, demonstrating the apparent yield gap between traditional farm practices and attainable yield through enhanced agronomic management. Narrow rows and the combination of P-S-Zn and K-B fertility were the factors that provided the most significant yield increases over the standard control. Increasing plant population from 79,000 to 109,000 plants ha–1 reduced the yield gap when all other inputs were applied at the enhanced level. However, increasing plant population alone did not increase yield when no other factors were enhanced. Some agronomic factors, such as narrow rows and availability of plant nutrition, become more critical with increasing plant population when density-induced stress is more significant. Changes in yield were dependent upon changes in kernel number. Kernel weight was the heaviest when all the management factors were applied at the enhanced level while only planting 79,000 plants ha–1. Conversely, kernel weight was the lightest when increasing population to 109,000 plants ha–1 while all other factors were applied at the standard level. The yield contribution of each factor was generally greater when applied in combination with all other enhanced factors than when added individually to the standard input system. Additionally, the full value of high-input agronomic management was only realized when matched with greater plant density.

Performance Evaluation of the WOFOST Model for Estimating Evapotranspiration, Soil Water Content, Grain Yield and Total Above-Ground Biomass of Winter Wheat in Tensift Al Haouz (Morocco): Application to Yield Gap Estimation

The main goal of this investigation was to evaluate the potential of the WOFOST model for estimating leaf area index (LAI), actual evapotranspiration (ETa), soil moisture content (SM), above-ground biomass levels (TAGP) and grain yield (TWSO) of winter wheat in the semi-arid region of Tensift Al Haouz, Marrakech (central Morocco). An application for the estimation of the Yield Gap is also provided. The model was firstly calibrated based on three fields data during the 2002–2003 and 2003–2004 growing seasons, by using the WOFOST implementation in the Python Crop simulation Environment (PCSE) to optimize the different parameters that provide the minimum difference between the measured and simulated LAI, TAGP, TWSO, SM and ETa. Then, the model validation was performed based on the data from five other wheat fields. The results obtained showed a good performance of the WOFOST model for the estimation of LAI during both growing seasons on all validation fields. The average R2, RSME and NRMSE were 91.4%, 0.57 m2/m2, and 41.4%, respectively. The simulated ETa dynamics also showed a good agreement with the observations by eddy covariance systems. Values of 60% and 72% for R2, 0.8 mm and 0.7 mm for RMSE, 54% and 31% for NRMSE are found for the two validation fields, respectively. The model’s ability to predict soil moisture content was also found to be satisfactory; the two validation fields gave R2 values equal to 48% and 49%, RMSE values equal to 0.03 cm3/cm3 and 0.05 cm3/cm3, NRMSE values equal to 11% and 19%. The calibrated model had a medium performance with respect to the simulation of TWSO (R2 = 42%, RSME = 512 kg/ha, NRMSE = 19%) and TAGP (R2 = 34% and RSME = 936 kg/ha, NRMSE = 16%). After accurate calibration and validation of the WOFOST model, it was used for analyzing the gap yield since this model is able to estimate the potential yield. The WOFOST model allowed a good simulation of the potential yield (7.75 t/ha) which is close to the optimum value of 6.270 t/ha in the region. Yield gap analysis reveals a difference of 5.35 t/ha on average between the observed yields and the potential yields calculated by WOFOST. Such difference is ascribable to many factors such as the crop cycle management, agricultural practices such as water and fertilization supply levels, etc. The various simulations (irrigation scenarios) showed that early sowing is more adequate than late sowing in saving water and obtaining adequate grain yield. Based on various simulations, it has been shown that the early sowing (mid to late December) is more adequate than late sowing with a total amount of water supply of about 430 mm and 322 kg (140 kg of N, 80 kg of P and 102 kg of K) of fertilization to achieve the potential yield. Consequently, the WOFOST model can be considered as a suitable tool for quantitative monitoring of winter wheat growth in the arid and semi-arid regions.