Causes Of Yield Gaps Research Articles

Effects of unbalanced fertilizer use on system productivity and profitability under rice‐based cropping systems: Evidence from Eastern Gangetic Plain

AbstractUnbalanced nutrient supply is one of the key causes of yield gaps and low farming profitability which impact food insecurity globally. However, the effects of an unbalanced nutrient supply on rice yields and farming profitability have not been quantified for diverse rice‐based cropping systems. To explore the effects, 412 respondents (mostly smallholder farmers) were surveyed in 2019 for the rice crops in three fully rice‐based cropping systems in four agro‐ecological zones of Bangladesh. Two robust estimation approaches namely propensity score matching and endogenous switching regression approaches were used to quantify the effects of unbalanced fertilizer use. The results show that about 68% of farmers in the study areas used unbalanced (over‐doses or under‐doses) nutrient rates relative to government‐endorsed recommendations. Adoption of recommended fertilizer rates was influenced by education, amount of organic manure used, off‐farm income, crop farming‐related training, organizational membership and lack of soil testing tendency. The findings also indicate that adopters of recommended nutrient rates received almost 16% and 46% higher system rice yield and system net return, respectively, relative to non‐adopters. However, based on our research findings, formulation and implementation of effective agricultural policies like effective extension services, strengthening soil testing facilities, increasing organizational membership and targeted awareness programmes could motivate smallholder rice farmers towards adoption of recommended nutrient rates. Adoption of recommended fertilizer rates can significantly enhance crop yields and farming profitability which can boost food security and rural livelihoods in the Eastern Gangetic Plain.

The effect of root cutting on the availability of organic carbon stock in oil palm plants (elaeis guneensis jacq)

Palm oil (Elaeis guenesis jacq) is an important commodity in Indonesia. Assessments of the underlying causes of yield gaps in oil palm production systems worldwide are lacking in exploring existing knowledge about oil palm productivity from a crop physiological perspective. Modifying the roots of oil palm plants through root cutting can affect the availability of carbon stocks in the soil because roots are one of the sources of organic carbon input into the soil. The purpose of the study was to analyses the effect of cutting the roots of oil palm plants on the availability of carbon stocks and soil fertility for these crops. The land where the study took place had a relatively low soil fertility status, there were several parameters observed showing low availability status in the soil. The low content of nutrients in the soil causes limited soil in providing nutrients to support plant growth.

Benchmarking Water-Limited Yield Potential and Yield Gaps of Shiraz in the Barossa and Eden Valleys

Background and Aims. Vineyard performance is impacted by water availability including the amount and seasonality of rainfall, evapotranspiration, and irrigation volume. We benchmarked water-limited yield potential (Yw), calculated yield gaps as the difference between Yw and actual yield, and explored the underlying environmental and management causes of these gaps. Methods and Results. The yield and its components in two sections of 24 Shiraz vineyards were monitored during three vintages in the Barossa zone (GI). The frequency distribution of yield was L-shaped, with half the vineyards below 5.2 t·ha−1, and an extended tail of the distribution that reached 24.9 t·ha−1. The seasonal ratio of actual crop evapotranspiration and reference evapotranspiration was below 0.48 in 85% of cases, with a maximum of 0.65, highlighting a substantial water deficit in these vineyards. A boundary function relating actual yield and seasonal rainfall was fitted to quantify Yw. Yield gaps increased with an increasing vine water deficit, as quantified by the carbon isotope composition of the fruit. The yield gap was smaller with higher rainfall before budburst, putatively favouring early-season vegetative growth and allocation to reproduction, and with higher rainfall between flowering and veraison, putatively favouring fruit set and berry growth. The gap was larger with higher rainfall and lower radiation between budburst and flowering. The yield gap increased linearly with vine age between 6 and 33 yr at a rate of 0.3 t·ha−1·yr−1. The correlation between yield gap and yield components ranked bunch weight ≈ berries per bunch > bunch number > berry weight; the minimum to close the yield gap was 185,000 bunches ha−1, 105 g bunch−1, 108 berries bunch−1, and 1.1 g berry−1. Conclusions. Water deficit and vine age were major causes of yield gaps. Irrigation during winter and spring provides an opportunity to improve productivity. The cost of dealing with older, less productive vines needs to be weighed against the rate of increase in yield gap with vine age. Significance of the Study. A boundary function to estimate water-limited yield potential returned viticulturally meaningful yield gaps and highlighted potential targets to improve vineyard productivity.

Developing an operational framework to diagnose yield gaps in commercial sugarcane mills

Yield-gap analysis provides farmers and crop consultants with key information to identify low-performance fields and tune agronomic practices to increase yield. The objective of this study was to develop an operational framework that allows quantification of yield gaps in individual sugarcane commercial fields (referred to as ‘blocks’) and identification of their causes. We provide proof of concept about application of our framework using a sugarcane mill in southern Brazil that manages ca. 8000 blocks per year as a case study. Water-limited yield potential (Yw) was estimated for each block using a crop model coupled with local weather, soil, and crop management data, and the yield gap was estimated as the difference between Yw and actual yield. To further illustrate application of the framework, causes for yield gaps for nine individual blocks were determined based on field measurements in combination with yield-loss models derived from the literature. Average yield gap was 34 Mg ha−1, which represented 33% of simulated Yw, indicating room for increasing sugarcane yields. Across the nine blocks where causes of yield gaps were assessed, nutrient limitations, insect damage, and sprouting failures accounted for 70% of the current yield gap. In contrast, weeds and diseases were less important. Causes for yield gaps can be categorized into those associated long-term management, such as inappropriate harvest practices leading to sprouting failures and soil compaction, and those related with short-term management such as nutrient application and control of insects, pests, and diseases. The study provides an operational framework that allows sugarcane mills to diagnose yield gaps across their blocks and identify opportunities to increase yield via better agronomic management.

The Yield Gap Maize under Intensive Cropping System in Central Java

The study aimed to estimate the level of the yield gaps of maize in major producing areas, point out the causes of yield gaps in farmers’ maize fields, and identify opportunities to the existing yield gaps through management practices of maize production in Central Java. This is the strategy for closing the existing yield gaps to achieve food self-sufficiency in agricultural land. Methods to estimates the yield gaps should cover data sources on physical conditions (weather and soil), management practices, and smallholder shapes. The relevant methods for estimating actual yields (Ya), potential (Yp), and water-limited (Yw) were compared. The yield gaps of maize under intensive cropping systems in rainfed ecosystems resulted in significant differences in all cultivation situations. The lowland rainfed maize showed Ya, Yp, and Yw values of 5.57, 12.83, and 12.47 ton/ha, respectively. The major causes of the yield gaps include variety, land preparation, and water issues concerned with the limited water inputs.

Assessing the contribution of nitrogen fertilizer and soil quality to yield gaps: A study for irrigated and rainfed maize in China

Yield gap (Yg) analysis is useful to map the scope for sustainable intensification of agriculture, but explaining and quantifying the underlying causes of yield gaps remains a considerable challenge. The objective of this study was to decompose maize yield gaps under different nitrogen (N) application rates and soil quality conditions across irrigated and rainfed cropping systems in China. A comprehensive database consisting of 5228 on-farm trials located in three major maize production regions of China was used for this purpose. The on-farm trials contained detailed information for four different treatments: fertilizer omission (control), optimal N rate (optimal N), 50% of optimal N rate (low N) and 150% of optimal N rate (high N). These were combined with biophysical and yield potential data from the Global Yield Gap Atlas ( http://yieldgap.org ). An analytical framework integrating stochastic frontier analysis and principles of production ecology was applied to decompose the overall maize yield gap into components of efficiency (and respective management and soil quality effects, Yg-M and Yg-S), resource (Yg-R), and technology Yg (Yg-T). The potential yield (Yp) of irrigated maize averaged 14.5 Mg/ha in Northeast China (NE) and 11.9 Mg/ha for North China Plain (NCP), and the water-limited potential yield (Yw) of rainfed maize averaged 12.0 Mg/ha in NE and 10.5 Mg/ha in Southwest China (SW), respectively. Maize yield gaps were highly variable across N treatments and cropping systems and ranged between 27–56% of Yp or Yw. Larger absolute yield gaps were observed in irrigated cropping systems in NE (4.8–8.1 Mg/ha) than in NCP (3.8–6.1 Mg/ha) and in rainfed cropping systems in NE (3.6–6.7 Mg/ha) and SW (2.8–5.9 Mg/ha). The components of the yield gap differed in size across cropping systems and N treatments. Yg-T was fairly small and consistent across N treatments ranging between 7.0% and 12.0% of Yp for irrigated maize and only ca. 2.0% of Yw for rainfed maize in NE. Yg-R was strongly associated with the N treatment explaining between 16.0–26.0% of Yp (or Yw) for control and low N treatments and being close to negligible for the optimal and high N treatments. Yg-M due to inefficient crop management accounted for 6.0–14.0% of Yp (or Yw) across cropping systems and N treatments, which is equivalent to 0.7–1.8 Mg/ha. The Yg-S explained the largest proportion of the total yield gap, especially in trials with low and medium soil quality levels, accounting for 11.0–24.0% of Yp or Yw (1.3–3.1 Mg/ha). The Yg-S was linked to partly manageable soil properties, such as low soil organic matter contents and low available P and/or K. This study is one of the first to incorporate the effects of soil quality in yield gap analysis and provides a basis to target management practices that can improve soil quality and N use efficiency while narrowing maize yield gaps in China.

On-farm assessment of eucalypt yield gaps - a case study for the producing areas ofthe state of Minas Gerais, Brazil.

The concept of yield gaps provides a basis for identifying the main sources of production losses, caused by water or management deficiencies, which may help foresters and forest companies to better plan and make decisions in their areas. Thus, the aim of this study was to identify the magnitude and the major causes of yield gaps of eucalypts, being this the most planted forest genus in Brazil, in different producing regions of the state of Minas Gerais that has the largest planted area. To these ends, potential (Yp) and attainable (Ya) yields were simulated using the agroecological zone model (AEZ-FAO) adapted and calibrated for Brazilian eucalypt clones. Actual yield (Yr) data were obtained from 22 sites located in the state of Minas Gerais from 2009 to 2016, considering an average forest rotation of 6.7 years and plantings occurring between 2002 and 2010. From this, the total yield gap (YGtot), yield gap by water deficit (YGwd), and yield gap by sub-optimal management (YGman) were determined. The YGwd ranged from 37 to 69 m3 ha-1 year-1 across the 22 sites assessed, with an average value of 55 m3 ha-1 year-1. On the other hand, the YGman ranged from zero (optimal management) to 31 m3 ha-1 year-1. The eucalypt yield gap in commercial areas of Minas Gerais state was mainly caused by water deficit, which represented 77% of the total yield gap. On the other hand, the deficiencies in forest management accounted for 23% of the total yield gap.

Boosting Rice Production Through Narrowing of Exploitable Yield Gap

The yield gap discussed in this paper is the difference between the potential farm yield and the actual average farm yield of rice is 1.92 t ha-1 in Bangladesh. The yield gap is mainly caused by physical, biophysical, socioeconomic and institutional factors. Different strategies, such as integrated crop management (improve adapted varieties, site specific nutrient management, locally adapted integrated weed management and optimized water management) practices, timely supply of inputs including credit to farmers, research and extension collaboration to transfer the new technologies have been discussed to minimize yield gap. Suggestions have been made to make credit available to resourcepoor small farmers to buy necessary inputs, reducing transaction cost, simplifying lending procedures and strengthening monitoring mechanism of the current credit system are, however, essential to enable the farmers to avail the credit facility. Efforts should be made to update farmers’ knowledge on the causes of yield gaps in crops and measures to narrow the gaps through training, demonstrations, field visits and monitoring by extension agencies to achieve high yield. Based on present yield gap of rice (1.92 t ha-1) at least Tk. 556 billion could be earned from the additional production annually by narrowing 40 % the yield gap.
 SAARC J. Agri., 18(2): 167-180 (2020)

On water‐use efficiency, boundary functions, and yield gaps: French and Schultz insight and legacy

AbstractThis article is part of a series of brief commentaries to highlight papers that have resulted in important and distinctly new perspectives in crop science. Here, we outline a series of two papers by French and Schultz (F&S) on water use efficiency of wheat (Triticum aestivum L.) published in 1984. The insight of F&S was not to draw a regression line across their yield‐evapotranspiration data. Instead they saw an upper limit of 20 kg ha−1 mm−1 that, less than a decade after publication, was widely used by Australian farmers to benchmark their crops and identify causes of yield gaps. Over the last two decades, F&S‐type benchmarks have been expanded beyond wheat and beyond Australia to include sunflower (Helianthus annuus L.) in Argentina, maize (Zea mays L.) and wheat in the United States and China, and millet [Pennisetum glaucum (L.) R. Br.] in West Africa, among others. More recently, the concept of yield gap analysis based on a upper limit of yield per unit resource uptake has been successfully extrapolated to N. The N uptake per millimeter of water required to meet the water‐limited yield potential (0.65 kg N ha−1 mm−1 under their original conditions) is a hidden insight in the work of French and Schultz; it has been recently rediscovered in the context of crop improvement, co‐limitation theory, and drought‐induced N deficit. Perhaps more deeply, French and Schultz shifted farmers mindset from the mildly fatalistic idea of “drought” to the notion that many other factors were typically limiting crop yield, and the quantitative challenge of attaining a plausible benchmark.

Quantifying rice yield gaps and their causes in Eastern and Southern Africa

AbstractThe demand for rice in Eastern and Southern Africa is rapidly increasing because of changes in consumer preferences and urbanization. However, local rice production lags behind consumption, mainly due to low yield levels. In order to set priorities for research and development aimed at improving rice productivity, there is a need to characterize the rice production environments, to quantify rice yield gaps—that is, the difference between average on‐farm yield and the best farmers’ yield—and to identify causes of yield gaps. Such information will help identifying and targeting technologies to alleviate the main constraints, and consequently to reduce existing yield gaps. Yield gap surveys were conducted on 357 rice farms at eight sites (19–50 farmers per site) across five rice‐producing countries in Eastern and Southern Africa—that is Ethiopia, Madagascar, Rwanda, Tanzania and Uganda—for one or two years (2012–13) to collect both quantitative and qualitative data at field and farm level. Average farm yields measured at the eight sites ranged from 1.8 to 4.3 t/ha and the average yield gap ranged from 0.8 to 3.4 t/ha. Across rice‐growing environments, major causes for yield variability were straw management, weeding frequency, growth duration of the variety, weed cover, fertilizer (mineral and organic) application frequency, levelling and iron toxicity. Land levelling increased the yield by 0.74 t/ha, bird control increased the yield by 1.44 t/ha, and sub‐optimal management of weeds reduced the yield by 3.6 to 4.4 t/ha. There is great potential to reduce the current rice yield gap in ESA, by focusing on improvements of those crop management practices that address the main site‐specific causes for sub‐optimal yields.

Assessing approaches for stratifying producer fields based on biophysical attributes for regional yield-gap analysis

Large databases containing producer field-level yield and management records can be used to identify causes of yield gaps. A relevant question is how to account for the diverse biophysical background (i.e., climate and soil) across fields and years, which can confound the effect of a given management practice on yield. Here we evaluated two approaches to group producer fields based on biophysical attributes: (i) a technology extrapolation domain spatial framework (‘TEDs’) that delineates regions with similar (long-term average) annual weather and soil water storage capacity and (ii) clusters based on field-specific soil properties and weather during each crop phase in each year. As a case study, we used yield and management data collected from 3462 rainfed fields sown with soybean across the North Central US (NC-US) during four growing seasons (2014–2017). Following the TED approach, fields were grouped into 18 TEDs based on the TED that corresponded to the geographic location of each field. In the cluster approach, fields were grouped into clusters based on similarity of in-season weather and soil. To evaluate how the number of clusters would affect the results, fields were grouped separately into 5, 10, 18, and 30 clusters. The two stratification approaches (TEDs and clusters) were compared on their ability to explain the observed yield variation and yield response to key management factors (sowing date and foliar fungicide and/or insecticide). Lack of stratification of producer fields based on their biophysical background ignored management by environment (M × E) interactions, leading to spurious relationships and results that are not relevant at local level. In the case of the cluster approach, a fine stratification (18 and 30 clusters) explained a larger portion of the yield variance compared with a coarse stratification (5 and 10 clusters). However, for our case study in the NC-US region, we did not find strong evidence that the data-rich clustering approach outperformed the TEDs on the ability to explain yield variation and identify M × E interactions. Only the stratification into 30 clusters exhibited a small improved ability at explaining yield variation compared with the TEDs. However, the use of the clustering approach had important trade-offs, including large amount of data requirements and difficulties to scale results to different regions and over time. The choice of the stratification method should be based on objectives, data availability, and expected variation in yield due to erratic weather across regions and years.

Over‐dispersed count data in crop and agronomy research

AbstractWhile evaluating plant response to biotic or abiotic stress and genotype–environment interactions and searching causes of yield gap, very often are observed data with non‐normal distributions. One of the commonly encountered types of variables with a non‐normal distribution is count data. Count data are defined as the type of observations which have a positive, non‐zero, integer value. The selection of appropriate probability distributions and model types is very important due to the risk of estimating the variance incorrectly—the phenomenon of over‐dispersion. Increasingly, biologists and agronomists have been using methods based on generalized linear models. However, sometimes, when including count data, they are not aware of or disregard their over‐dispersion. One of the solutions for over‐dispersed count data is to use probability distributions and model types which assume a more flexible mean and variance relationship. Thus, the aim of this study is to present various ways of assessing over‐dispersion. Additionally, we present alternative distributions and discuss other approaches to solve the problem of over‐dispersion in count data sets. As examples in this study are used real data sets from different agricultural experiments. In our study, in one out of the two data sets used, this phenomenon occurred. Thus, in the analysis of count data, instead of using default distribution (usually Poisson distribution), other distributions should be considered because of the possible occurrence of over‐dispersion. We also observed that there is not one universal distribution to use and each data set might need a separate assessment to choose its distribution. For an efficient and proper count data analysis, with potential over‐dispersion, it is important to explore several options, i.e. evaluate models with an alternative to Poisson probability distributions and then make an informed choice.

Socio-psychological and management drivers explain farm level wheat yield gaps in Australia

Achieving sustainable global food security for a rapidly growing world population is one of the greatest challenges of our time. Producing more food efficiently by closing the yield gaps is regarded as a promising solution to address this challenge without further expanding farming land. However, there is limited understanding of the causes contributing to yield gaps. The present study aimed to comprehensively examine three dimensions of the causes for the wheat yield gaps in Australia: farm management practices, farm characteristics and grower characteristics. Computer-assisted telephone interviews of 232 wheat producers from 14 contrasting local areas were conducted. The data collected on these three dimensions were used to develop a comprehensive framework to understand causes of yield gaps. Results reveal significant differences between farms with smaller yield gaps and those with greater yield gaps in relation to farming management as well as farm and grower characteristics. Findings further underline that farms with smaller yield gaps are likely to be smaller holdings growing less wheat on more favourable soil types, are more likely to apply more N fertiliser, to have a greater crop diversity, to soil-test a greater proportion of their fields, to have fewer resistant weeds, to adopt new technologies, and are less likely to grow wheat following either cereal crops or a pasture. They are more likely to use and trust a fee-for-service agronomist, and have a university education. The dynamic relationships between grower characteristics and farm management practices in causing yield gaps are further highlighted through a path analysis. This study is the first to demonstrate that yield gaps are the result of the intertwined dynamics between biophysical factors, grower socio-psychological characteristics and farm management practices. Socio-psychological factors not only directly contribute to yield gaps, but they also influence farm management practices that in turn contribute to yield gaps. Our findings suggest that, to close wheat yield gaps, it is important to develop integrated strategies that address both socio-psychological and farm management dimensions.

Cluster analysis of polyisoprenoid in oil palm (Elaeis guineensis) leaves in different land-uses to find the possible cause of yield gap from planting materials

Basyuni M, Wati R, Deni I, Tia AR, Slamet B, Siregar ES, Syahputra I. 2018. Cluster analysis of polyisoprenoid in oil palm (Elaeis guineensis) leaves in different land-uses to find the possible cause of yield gap from planting materials. Biodiversitas 19: 1492-1501. The distribution and occurrence of polyprenols and dolichols in the leaves of oil palm (Elaeis guineensis Jacq.) plantations in different land-uses in North Sumatra, Indonesia were analyzed using two-dimensional thin layer chromatography (2D-TLC). Eighty-one of oil palm leaves were sampled to represent twenty-seven sites of land-uses, namely paddy field (four locations), mangrove (three locations) in Lubuk Kertang, Langkat, North Sumatra. In addition, samples from four groups of smallholders in Stabat, Langkat, six sites in Bangun Bandar, Serdang Bedagai, and ten sites on the campus of Universitas Sumatera Utara (USU), North Sumatra were collected. In the leaves, only one type (type II) with respect to the distribution of polyisoprenoids was detected: having the presence of both polyprenols and dolichols. Either type I, having predominance of dolichols over polyprenols or type III, displaying dominating polyprenols over dolichols were not observed. Results also showed that chain-length distribution of ficaprenols (C50-C60) without longer polyprenols (C85-C100) and dolichols of C85-C100 was detected in the paddy field, mangroves, and one site in USU campus. This polyisoprenoid profile was close to dura type of Elaeis guineensis. By contrast, the remaining land-uses had ficaprenols and longer polyprenols, and dolichols (C85-C100), which belong to tenera or pisifera type. To confirm this finding, a dendrogram was constructed. Cluster analysis demonstrated that twenty-seven sites of E. guineensis were grouped into appropriate types of dura, pifiera, and tenera accordingly, indicating that the existence of polyisoprenoids in E. guineensis was a chemotaxonomic marker. The finding of polyisoprenoid pattern of E. guineensis as dura type in mangrove and paddy field sites may reveal significant causes of yield gap in oil palm plantation from planting materials.

Yield gap analysis and entry points for improving productivity on large oil palm plantations and smallholder farms in Ghana

Oil palm production must increase in Ghana to meet the increasing demand for palm oil and avoid costly imports. Although maximum fruit bunch (FB) yields of >20 t ha−1 yr−1 are achievable, average FB yields in Ghana are only 7 t ha−1 yr−1. Despite the pressing need to increase palm oil production and improve yields, knowledge of the underlying causes of poor yields in Ghana is lacking. Closing yield gaps in existing plantings in smallholdings and plantations offers great opportunities to increase oil production without area expansion, thus sparing land for other uses. This study sought to understand the magnitude and underlying causes of yield gaps in plantation and smallholder oil palm production systems in Ghana based on a detailed characterization of management practices and yield measurements over a two-year period. Using a boundary line analysis, the water-limited yield (Yw) over a planting cycle was defined as about 21 t ha−1 yr−1 FB, with yield gaps of 15.4 t ha−1 yr−1 FB at smallholder farms and 9.8 t ha−1 yr−1 FB at plantations. Poor management practices, including incomplete crop recovery (i.e., harvesting all suitable crop) and inadequate agronomic management were the main factors contributing to these yield gaps. Productivity losses were further exacerbated by low oil extraction rates by small-scale processors of 12% as compared to 21% by the large-scale processors. The potential losses in annual crude palm oil (CPO) during the crop plateau yield phase therefore exceed 5 and 3 t ha−1 yr−1 for small-scale and large-scale production systems respectively. Investment to reduce yield gaps by appropriate agronomic and yield recovery practices across all production systems, while improving access of smallholder producers to more efficient oil palm processing facilities, can make a significant contribution to closing the supply gap for palm oil in Ghana. The impact of such investments on large-scale plantations could result in a doubling of CPO production. Smallholder farmers could benefit the most with a fourteen-fold increase in CPO production and economic gains of >1 billion US$.

Sugarcane yield gap analysis in Brazil – A multi-model approach for determining magnitudes and causes

Brazil is the largest sugarcane producer in the world playing a pivotal role on global ethanol production. The sugarcane yield levels across the producing regions of the country vary substantially, resulting in yield gaps of different magnitudes, which represent a huge opportunity for increasing sugarcane and ethanol production. According to that, the present study aimed to investigate the sugarcane yield gap in Brazil, their magnitude and causes (water deficit or crop management), considering a multi-model approach. Three different sugarcane simulation models, FAO-Agroecological Zone, DSSAT/CANEGRO and APSIM-Sugarcane, properly calibrated and validated for sugarcane in Brazil, were used to estimate potential and water-limited yields and yield gaps for 30 locations across the country. The average of total yield gap for the 30 locations was 124 t ha−1. The main proportion of the total yield gap was caused by water deficit (about 89 t ha−1, representing 73% of the total), followed by sub-optimal crop management (about 35 t ha−1, representing 27% of the total). The highest yield gap by water deficit was found in the Northeastern region, whereas São Paulo State showed the lowest yield gap by crop management. The main causes of yield gap by crop management are possibly related to the production system based on long-term monoculture and soil compaction due to intense crop mechanization in recent years. Reducing sugarcane yield gap caused by crop management by 20 to 100% would allow to diminish the cultivated area with this crop, respectively, from 9 to 32%. Possible solutions to mitigate the yield gaps, such as use of irrigation, adoption of drought tolerant cultivars, better traffic control, periodical crop rotation, among others, were also discussed.

Benchmarking wheat yield against crop nitrogen status

Availability of nitrogen and water are major constraints for crop yield, and their interactions are manyfold. Yield gap analysis in rainfed systems commonly uses water-limited yield potential (Yw) as a benchmark; benchmarking against nitrogen-limited yield potential (YN) is less common. The aim of this study was to benchmark wheat yield against YN in winter-rainfall regions of Australia. We established experiments, and sampled both farmers’ fields and National Variety Trials spanning wide ranges of soils, management practices, and water supply (seasonal rainfall + irrigation) from 153 to 759 mm in South Australia, and from 178 to 428 mm in Western Australia. We measured yield, quantified crop nitrogen nutrition index (NNI) at stem elongation, anthesis or both, derived boundary functions relating YN and NNI, calculated yield gaps as the difference between YN and actual yield, and explored the associations between yield gaps and environmental, crop and agronomic factors.In South Australia, NNI at anthesis ranged from 0.45 to 1.45 and yield from 0.9 to 8.9 t ha−1; in Western Australia NNI at stem elongation ranged from 0.27 to 1.16 and yield from 1.0 to 7.1 t ha−1. Bi-linear boundary functions were fitted with a linear YN – NNI phase up to a NNIx threshold (0.95 ± 0.134 in South Australia, 0.87 ± 0.145 in Western Australia), and a plateau reflecting yield potential (7.8 ± 0.38 t ha−1 in South Australia, 6.5 ± 0.52 t ha−1 in Western Australia). Similar bi-linear boundaries, with congruent NNIx (≈0.9), were found for grain number and shoot biomass at maturity. In South Australia, water supply explained 54% of the yield gap, which declined linearly from about 6 t ha−1 to zero with increasing water availability. Further, the yield gap correlated negatively with carbon isotope discrimination at anthesis, a direct measure of crop water status. In Western Australia, the direct association between yield gap and rainfall was weak, but there was an indirect agronomic link, where low seeding rate in low rainfall environments contributed to low biomass at stem elongation, which in turn explained a large part of the yield gap.In two out of five National Variety Trials, where the aim is comparing the yield of current and emerging varieties, crops were nitrogen deficient. This is a potential source of bias as some varieties were above and others below the NNIx threshold of nitrogen sufficiency. Supply of nitrogen in varietal comparisons needs attention.The approach advanced in this paper can be applied to benchmark yield against crop nitrogen status and identify causes of yield gaps, and for in-season quantification of crop nitrogen status to assist fertiliser decisions directly, or indirectly as a reference for spectral indices.

Yield gap analysis of feed-crop livestock systems: The case of grass-based beef production in France

Sustainable intensification is a strategy contributing to global food security. The scope for sustainable intensification in crop sciences can be assessed through yield gap analysis, using crop growth models based on concepts of production ecology. Recently, an analogous cattle production model named LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems – Beef cattle) was developed, which allows yield gap analysis in beef production systems. This paper is the first to assess yield gaps of integrated feed-crop livestock systems, to analyse underlying causes of yield gaps, and to identify feasible improvement options. We used grass-based beef production in the Charolais area of France as a case study. To this end, we combined LiGAPS-Beef with crop growth models that simulate grass production (fresh grass under grazing, grass silage, hay) and wheat production (concentrate). Feed crop and cattle production were integrated to simulate potential and resource-limited live weight (LW) production per hectare. Potential production is defined as the theoretical maximum LW production per ha, in the absence of resource or management limitations. Resource-limited production is determined by availability of one or several resources: water and nutrients for crops, and feed quality and quantity for animals. Potential production of a cattle herd with an ad libitum diet of grass silage was 2380kgLWha−1year−1 and resource-limited production was 664kgLWha−1year−1. Actual LW production (354kgLWha−1year−1) was 15% of the potential production, implying a relative yield gap of 85%, and 53% of the resource-limited production, implying a relative yield gap of 47%. Applying yield gap analysis disentangled the major biophysical causes of these yield gaps: feeding diets other than the ad libitum grass silage diet, water-limitation in feed crops, and sub-optimal management. These yield gaps suggest scope to intensify beef production. We demonstrate, however, that yield gap mitigation decreased the operational profit per kg LW under the European regulations for bovine and grassland premiums operational in 2014. Hence, as expected, the premiums aiming to support farmers' income and to promote sustainable agriculture and rural development were not conducive to narrow yield gaps at the same time. The current common agricultural policy (CAP, 2015–2020) provides more scope for intensification, such as increasing stocking density via better grassland management.

Comparative yield gaps, economic impact and constraint analysis in front line demonstrations of soybean and black soybean in Uttarakhand hills

Soybean and black soybean are important kharif crops which play a vital role in nutritional and livelihood security in Uttarakhand hills. Considering the scope of improvement in productivity through the improved varieties and recommended production technologies, front line demonstrations were conducted by ICAR-VPKAS, Almora during Kharif 2015 and 2016. The present study revealed that the average technology gap for black soybean (1.58 q/ha) was lower than soybean (7.87 q/ha) at farmer’s field which is suggesting the need to further develop better performing varieties in black soybean than existing ones as the potential yield levels of improved varieties are easily achievable in given environment. The extension gap for black soybean (5.57 q/ha) and soybean (6.25 q/ha), exhibiting the potential of improving productivity by understanding the causes of yield gaps and abridging them with proper interventions. In terms of economic impact based on benefit cost ratio in front line demonstrations, the cultivation of black soybean (1.0) was found more profitable than soybean (0.94) besides, better feasibility of improved technological interventions in black soybean at farmer’s field revealed by lower technology index of black soybean (12.72%) than soybean (39.10%). An insight on various constraints causing yield gaps and potential interventions to mitigate them have also been discussed.

Assessing causes of yield gaps in agricultural areas with diversity in climate and soils

Identification of causes of gaps between yield potential and producer yields has been restricted to small geographic areas. In the present study, we developed a novel approach for identifying causes of yield gaps over large agricultural areas with diversity in climate and soils. This approach was applied to quantify and explain yield gaps in rainfed and irrigated soybean in the North-Central USA (NC USA) region, which accounts for about one third of soybean global production. Survey data on yield and management were collected from 3568 producer fields over two crop seasons and grouped into 10 technology extrapolation domains (TEDs) according to their soil, climate, and water regime. Yield potential was estimated using a combination of crop modeling and boundary functions for water productivity and compared against highest producer yields derived from the yield distribution in each TED-year. Yield gaps were calculated as the difference between yield potential and average producer yield. Explanatory factors for yield gaps were investigated by identifying management practices that were concordantly associated with high- and low-yield fields. Management×TED interactions were then evaluated to elucidate the underlying causes of yield gaps. The chosen spatial TED framework accounted for about half of the regional variation in producer yield within the NC USA region. Across the 10 TEDs, soybean average yield potential ranged from 3.3 to 5.3 Mgha−1 for rainfed fields and from 5.3 to 5.6Mgha−1 for irrigated fields. Highest producer yields in each TED were similar (±12%) to the estimated yield potential. Yield gap, calculated as percentage of yield potential, was larger in rainfed (range: 15–28%) than in irrigated (range: 11–16%) soybean. Upscaled to the NC USA region, yield potential was 4.8Mgha−1 (rainfed) and 5.7Mgha−1 (irrigated), with a respective yield gap of 22 and 13% of yield potential. Sowing date, tillage, and in-season foliar fungicide and/or insecticide were identified as explanatory causes for yield variation in half or more of the 10 TEDs. However, the degree to which these three factors influenced producer yield varied across TEDs. Analysis of in-season weather helped interpret management×TED interactions. For example, yield increase due to advances in sowing date was greater in TEDs with less water limitation during the pod-setting phase. The present study highlights the strength of combining producer survey data with a spatial framework to measure yield gaps, identify management factors explaining these gaps, and understand the biophysical drivers influencing yield responses to crop management.