Water-limited Potential Yield Research Articles

Coupling field monitoring with crop growth modelling provides detailed insights on yield gaps at field level: A case study on ware potato production in the Netherlands

ContextYield gap analyses are useful to assess and benchmark the productivity of cropping systems. Often such analyses are performed at higher aggregation levels. As a result, these studies lack the detail to explain yield gaps at field level and hence make it difficult to translate findings into precise recommendations to farmers and extensionists. ObjectiveThis study provides a detailed approach for yield gap assessments at field level through coupling frequent field monitoring in farmers’ fields with crop growth modelling. We used ware potato production in the Netherlands as a case to study yield gaps at field level, as average productivity is high whilst yields are still highly variable among fields, and as ware potato is an important cash crop for farmers. MethodsOver two growing seasons, 96 ware potato fields were monitored throughout the growing season on a biweekly basis, taking measurements on soil, crop growth and yield. The crop growth model SWAP-WOFOST was used to simulate potential and water-limited potential yields. Various statistical methods were used to quantify yield gap explaining factors. ResultsThe average yield gap ranged from 20 to 31% depending on the year and soil type. Among fields, the yield gap ranged from 0 to 51%. On clayey soils, the yield gap was attributed mostly to oxygen stress. On sandy soils, the yield gap was determined mostly by drought stress in 2020, a relatively dry year, and by reducing factors (pests, diseases and poor agronomic practices) in 2021, an average year in terms of precipitation. The type of reducing factors differed per field. Furthermore, we found that earlier planting and later harvesting can increase yields, as Yp is radiation-limited. ConclusionsOverall, there is limited scope to narrow the yield gap as current ware potato production is already close to 80% of the potential yield, which is assumed to be approximately the maximum farmers can attain. However, yield and resource use efficiency gains are to be made for individual fields. Furthermore, we conclude that frequent field monitoring coupled with crop growth modelling is a powerful way to assess yield gap variability and to get detailed insight in the yield gap explaining factors at field level. SignificanceThis study showed that coupling frequent field monitoring with crop growth modelling allows to gain detailed insight in yield gap variability among fields. This method provides detailed information about yield gap explaining factors which can be used to improve yield and resource use efficiency at field level.

A modelled quantification of reduced nitrogen fertiliser requirement and associated trade-offs from inclusion of legumes and fallows in wheat-based crop sequences

ContextGlobally there is a need to increase crop production and maintain soil fertility while reducing environmental impact. Intensive wheat production is dependent on synthetic nitrogen (N) fertilisers which represent ∼38% of total on-farm greenhouse gas emissions in Australia and contribute to N pollution. There are four major sources of N available for crop production 1) N mineralised from native soil organic matter (SOM) 2) N derived from biological fixation by legume-associated rhizobial bacteria 3) synthetic N fertiliser 4) N-rich manures, composts, biosolids and other organic wastes. Farmers can emphasise use of these different sources through their management. ObjectivesIn this study we use crop simulation to predict the minimum amount of N fertiliser required for semi-arid wheat crops to achieve economic yield (EY – defined as 80% of water limited potential yield, PYw) when grown in long-term (1991–2020) sequences and at different rotational intensities with either; grain legumes (GL, source 2), legume winter cover crops (CC, source 2) and winter fallow (WF, source 1) compared to continuous wheat (CW, source 3). Results and conclusionsLegume and fallow phases at 25–67% intensity (e.g. one out of four to two out of three crops) reduced the long-term mean fertiliser N application required to achieve EY. For every % area increase of GL and CC, the mean N applied to achieve EY was reduced by 1.3 kg N kg ha-1 (0.43–1.9 N kg ha-1). For every % area increase of WF the mean N applied to achieve EY was reduced by 1.1 kg N kg ha-1 (0.4–1.6 N kg ha-1). Global warming potential emissions from soil (CO2 and N2O) increased compared to CW when legume intensity increased in high rainfall environments with high SOM but decreased at increasing intensity in low rainfall environments with low SOM. Food energy production (kJ ha-1yr-1) was reduced relative to CW by inclusion of CC and WF and to a lesser extent GL in sequences due to reduced total grain production over the study period. However, protein yield increased as GL intensity increased relative to CW. The most profitable sequences over a 30-year period were CW, or GL at different intensities depending on environment. ImplicationsDiversification of wheat-based cropping systems with legumes reduces N fertiliser requirement, but depending on farming system context this benefit can trade off against production, profitability and environmental outcomes. Evaluation and exploitation of appropriate farming system contexts to achieve mutually beneficial outcomes is required to allow farmers and policy makers to make informed decisions on how to increase food production whilst minimising environmental impacts.

Site Factors Related to Dry Farm Vegetable Productivity and Quality in the Willamette Valley of Oregon

Dry farming has been defined as rainfed crop production in a climate with more than 20 inches of annual precipitation, but where most precipitation falls outside the growing season. Dry farming is garnering interest in the western United States because it allows farmers to produce crops despite a lack of access to irrigation or water rights or to eliminate the infrastructure, labor, and energy costs of irrigation systems. Sites have differing suitability for dry farming, and some sites that can be farmed with irrigation will perform poorly when dry-farmed. To determine site factors associated with dry farm yield and fruit quality, trials of ‘Early Girl’ tomato (Solanum lycopersicum) and ‘North Georgia Candy Roaster’ winter squash (Cucurbita maxima) were conducted at 17 participant farms in the Willamette Valley in Oregon, USA, in 2018 and 2019. The mean blossom-end rot (BER) incidence was higher in the Willamette Valley than in coastal California; this was probably because of the Willamette Valley’s hotter and drier climate. Increasing the available water-holding capacity of soil, total available water (available water-holding capacity of the soil plus in-season rainfall), native productivity rating, soil pH (0–6 inches and 24–36 inches), soil nutrient concentrations (0–6 inches and 24–36 inches), and in-season rainfall were positively associated with at least one measure of tomato or winter squash yield, fruit number, or average fruit weight. An earlier planting date was positively associated with winter squash total yield and total fruit number in 2019. The water-limited yield potential (the total yield potential if water was the only limiting factor) for 20-ft2/plant plots was estimated to be 2.2 tons/acre per inch for tomato and 2.8 tons/acre per inch for winter squash. In 2019, high-density plantings (20 ft2/plant) had higher tomato and winter squash mean total yields, mean total fruit numbers, and mean tomato unblemished yield than low-density plantings (40 ft2/plant). In 2019, planting tomato at 20 ft2/plant decreased the mean BER incidence by 15.6% when compared with planting tomato at 40 ft2/plant.

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.

Use of interpretive machine learning and a crop model to investigate the impact of environment and management on soybean yield gap

Context Management and environmental conditions are the main factors influencing yield of soybean (Glycine max (L.) Merr.). Despite an increase in average soybean yield in recent years in Iran, a considerable gap remains between actual yield and potential yield. Aims The objective of this study was to identify critical climate and management factors affecting soybean yield in Iran’s major soybean production area. Methods A combination of machine learning approaches (using gradient boosted decision trees, XGBoost) and the SSM-iCrop2 simulation model was used. Critical management factors affecting soybean yield were determined through interpretive machine learning using information collected from 268 soybean fields over a 5-year period. Potential yield and water-limited potential yield at six weather stations were estimated for 30 years via the SSM-iCrop2 simulation model. Water limitation was determined by considering the ratio of water-limited yield potential to potential yield, and heat stress status was quantified as the number of days with maximum temperature >36°C during the soybean growing season. Key results The XGBoost models adequately described the observed changes in soybean yield. Root-mean-square error and Lin’s concordance correlation coefficient values of the calibrated model were 262 kg ha−1 and 0.96, respectively, which indicated that the predictor variables could describe most of the variation in soybean yield for the studied dataset. Conclusions We identified 15 climatic and management variables that affect soybean yield. A large part of the studied area is under high water stress and low heat stress. Implications Optimal planting date and improved irrigation management are the main options for reducing the yield gap in the study area.

Exploring adaptation responses of maize to climate change scenarios in southern central Rift Valley of Ethiopia

In this study, we assessed responses of adaptation options to possible climate change scenarios on maize growth and yield by using projections of 20 coupled ensemble climate models under two representative concentration pathways (RCPs) 4.5 and 8.5 by means of a DSSAT model. Growth and yield simulations were made across present and future climate conditions using the hybrid maize variety (Shone). Subsequently, simulated yields were compared with farmer’ average and on-farm trial yields. Results showed that on-farm trial yield (5.1–7.3 t ha−1) lay in between farmers’ average yield (2.9–5 t ha−1) and water-limited potential yield (6.3–10.6 t ha−1). Maize yields achieved in farmers’ fields are projected to decline towards mid-century and further towards the end of the century regardless of the adaptation options compared with baseline in low potential clusters. Results of a combination of adaptation options including February planting, use of 64 kg ha−1 N and conservation tillage provided yield advantage of 5.8% over the 30 cm till under medium GHGs emission scenario during mid-century period at Shamana. Mulching with 5 t ha−1 was projected to produce a 4–5% yield advantage in the Hawassa cluster during the mid-century period regardless of changes in tillage or planting window. Under a high GHGs emission scenario, over 13.4% yield advantage was projected in the Bilate cluster due to conservation tillage and June planting during the mid-century period. In the Dilla cluster, the use of 10 t ha−1 mulch, conservation tillage and early planting (February) would result in a 1.8% yield advantage compared with the control either in medium or high GHGs emission scenarios. Thus, the most promising and least risky practices among simulated strategies were the use of nitrogen and mulching in combination with tillage or planting date adjustment. However, adaptation options remained least promising and highly risky if not integrated with mulching or nitrogen use. Hence, the negative impacts of future climate change and subsequent yield gaps would be reduced by optimizing the application of nitrogen, mulch and their interaction with planting date and tillage in high and low potential areas of maize production.

Alfalfa water productivity and yield gaps in the U.S. central Great Plains

Yield gap (Yg) analyses using farmer-reported yield and management data have been performed for a number of annual grain crops, but it lacks for perennial forages. The U.S. accounts for 21 % of the global alfalfa production with a large rainfed area located in the central Great Plains, serving as an interesting case-study for Yg in perennial forages. Most existing alfalfa Yg analyses quantified the magnitude of the Yg but failed to identify associated management practices to reduce it. Challenging this analysis, a systematic benchmark for alfalfa water productivity [WP, kg dry matter per mm evapotranspiration (ETc)] that allows for the quantification of Yg in farmer fields does not exist. Our objectives were to (i) benchmark alfalfa WP, (ii) quantify Yg in alfalfa farmer fields, and (iii) identify management opportunities to improve alfalfa yield. We conducted a systematic review of literature and compiled a database on alfalfa yield and ETc (n = 68 papers and 1027 treatment means) from which a WP boundary function was derived. We collected management and yield data from 394 commercial rainfed alfalfa fields during 2016–2019 in central Kansas. We then used satellite imagery to define the growing season (and corresponding water supply) for each field. The boundary function was then used to calculate Yg of each field, and conditional inference trees (CIT) explored the impact of management practices associated with increased yield. Our boundary function suggested an alfalfa WP of 34 kg ha-1 mm-1. Farmer-reported yield ranged from 0.9 to 19.0 Mg ha-1, averaging 7.6 Mg ha-1. Alfalfa water-limited yield potential (Yw) ranged from 11.1 to 23.2 Mg ha-1, resulting in an average yield gap of 54–60 % of Yw. Row spacing, seeding rates, and management of phosphorus fertilizer were major agronomic practices explaining alfalfa yields in farmer fields, followed by surrogate variables as sowing season, stand age, and soil pH. Our study provided the first systematic analysis estimating attainable alfalfa WP as function of ETc, suggesting that large alfalfa Yg exist in the U.S. central Great Plains. We also identified key agronomic practices associated with increased alfalfa yield. The WP here derived can be used for future studies aiming at quantifying alfalfa Yg across the globe. This was an initial step in quantifying Yg and its associated causes at farmer fields, and we highlight limitations and future directions for perennial forages yield gap analyses.

Yield Gaps of Major Cereal and Grain Legume Crops in Ethiopia: A Review

In Ethiopia, smallholder farmers are responsible for most food production. Though yield levels in grain crops have improved greatly over the years, they are still much lower than their potential. The source of yield improvements and the causes of those yield gaps are not well understood. To explain the drivers of yield gaps and current sources of yield improvements in four major cereals (teff, maize, wheat, and sorghum) and three grain legumes (faba bean, common bean, and soybean), we accessed the databases of the Global Yield Gap Atlas, the Food and Agriculture Organization of the United Nations, and the Central Statistical Agency of Ethiopia. Refereed journal articles and grey literature were sought in online databases using keywords. The results showed large increases in production of grain crops with little or no increase in areas of production. The yield increases were primarily attributed to genetic gain rather than agronomic improvements. Farmers’ yields remain far lower than those from on-farm trials and on-station trials and the calculated water-limited yield potential. Currently, yields of wheat, maize, sorghum, and common bean in Ethiopia are about 26.8, 19.7, 29.3, and 35.5% of their water-limited yield potentials. Significant portions of the yield gaps stem from low adoption and use of improved varieties, low application of inputs, continual usage of un-optimized crop management practices, and uncontrolled biotic and abiotic stresses. Proper application of fertilizers and use of improved varieties increase yield by 2 to 3 fold and 24–160%, respectively. Cereal-legume intercropping and crop rotation practices increase yield while reducing severity of pests and the need for application of synthetic fertilizers. In contrast, abiotic stresses cause yield reductions of 20–100%. Hence, dissection of the water-limited yield gap in terms of technology, resource, and efficiency yield gaps will allow the prioritization of the most effective intervention areas.

Yield Gap Analysis of Alfalfa Grown under Rainfed Condition in Kansas

The yield and production of alfalfa (Medicago sativa L.) have not been significantly improved in Kansas for the last 30 years even though farmers are using improved varieties. We have noted a significant yield difference between average alfalfa yield reported by farmers and researchers. The magnitude of yield gap in Kansas and its underlying factors are still unknown. Thus, understanding of potential yield is essential to meet the future forage demand with the limited production resources. The main objective of this study was, therefore, to quantify the current yield gap and identify the main yield-limiting factor for rainfed alfalfa grown in Kansas. To achieve this objective, we selected 24 counties in Kansas based on the rainfed production area and total production, and used county-level yield, daily temperature, and rainfall data from the past 30 yrs (1988–2017) of those selected counties. We applied four statistical approaches: (i) probability distribution function to delineate county-level alfalfa growing season, (ii) stochastic frontier yield function to estimate optimum growing season rainfall (GSR) and attainable yield, (iii) linear boundary function to estimate minimum water loss, water use efficiency, and water-limited potential yield, and (iv) conditional inference tree to identify the major yield contributing weather variables. The probability distribution function delineated the alfalfa growing season starting from mid-March to mid-November in Kansas. The frontier model estimated the attainable yield of 9.2 Mg ha−1 at an optimum GSR of 664 mm, generating a current yield gap of 18%. The linear boundary function estimated the water-limited potential yield of 15.5 Mg ha−1 at an existing GSR of 624 mm, generating a yield gap of 50%. The conditional inference tree revealed that 24% of the variation in rainfed alfalfa yield in Kansas was explained by weather variables, mainly due to GSR followed minimum temperature. However, we found only 7% GSR deficit in the study area, indicating that GSR is not the only cause for such a wide yield gap. Thus, further investigation of other yield-limiting management factors is essential to minimize the current yield gap. The statistical models used in this study might be particularly useful when yield estimation using remote sensing and crop simulation models are not applicable in terms of time, resources, facilities, and investments.

Closing rice yield gaps in Africa requires integration of good agricultural practices

Despite decades of international research and development efforts focusing on increased rice production in Africa, there is large yield gap, and the local production still needs to be complemented by rice imports to meet consumption demands. This paper aims to provide an overview of published research findings on rice yield gaps and the effects of ‘good agricultural practices’ (GAPs) on rice yield and nutrient use efficiency. The majority of previous studies were from irrigated lowlands, and quantified rice yield gaps as farmer-based (difference between ‘actual yield’ and ‘best farmers’ yield’) and model-based yield gaps (difference between ‘actual yield’ and ‘potential yield for irrigated and water-limited potential yield for rainfed rice’). The mean farmer- and model-based yield gaps were 3.1 and 5.0 t ha–1 for irrigated lowland (IL); 3.1 and 7.7 t ha–1 for rainfed lowland (RL); 2.0 and 6.0 t ha–1 for rainfed upland (RU), respectively in Africa. An analysis of studies from literature on GAPs in Africa revealed that most studies (64 %) were from IL and a wide range of yield increases following individual components of GAPs across environments. A median yield increase of 1.0 t ha–1 was achieved with improved water conservation practices in IL, whereas improved weed management increased yields by 0.7 t ha–1, mainly in IL compared to farmers’ practices. Application of inorganic fertilizers and/or organic amendments increased the yield by 0.8–1.2 t ha–1 across the environments. Integration of GAPs increased the median yield by 2.1 and 1.5 t ha–1 in IL and RL, respectively. The calculated agronomic efficiency of N, P and K from fertilizer experiments were within the desirable levels mainly in the IL and RL environments and comparable to the values from similar environments in Asia. For instance, the agronomic efficiency of N was 21, 13 and 12 kg kg–1, respectively for IL, RL and RU in Africa. Although rice yield gaps in Africa can be substantially reduced by introduction of integrated GAPs to farmers, there is large difference between model-based yield gaps and yield gain obtained by integrated GAPs. Further efforts are needed to identify factors causing this difference. We recommend a research and development focus on rainfed lowland rice systems, which have the largest model-based yield gaps by partially converting them to irrigated systems, and on improving nutrient use efficiencies and closing nutrient cycles.

Yield gap analysis of rainfed alfalfa in the United States.

The United States (US) is the largest alfalfa (Medicago sativa L.) producer in the world. More than 44% of the US alfalfa is produced under rainfed conditions, although it requires a relatively high amount of water compared to major field crops. Considering that yield and production of rainfed alfalfa have been relatively stagnant in the country for decades, there is a need to better understand the magnitude of yield loss due to water limitation and how far from yield potential current yields are. In this context, the main objective of this study was to estimate the current yield gap of rainfed alfalfa in the US. We collected 10 year (2009–2018) county-level government-reported yield and weather data from 393 counties within 12 major US rainfed alfalfa producing states and delineated alfalfa growing season using probabilistic approaches based on temperature thresholds for crop development. We then calculated county-level growing season rainfall (GSR), which was plotted against county-level yield to determine attainable yield (Ya) using frontier function analysis, and water-limited potential yield (Yw) using boundary function analysis. Average and potential water use efficiencies (WUE) were estimated, and associated yield gap referring to attainable (YGa) or water-limited yields (YGw) were calculated. Finally, we used conditional inference trees (CIT) to identify major weather-related yield-limiting factors to alfalfa forage yield. The frontier model predicted a mean Ya of 9.6 ± 1.5 Mg ha−1 and an associated optimum GSR of 670 mm, resulting in a mean YGa of 34%. The boundary function suggested a mean Yw of 15.3 ± 3 Mg ha−1 at the mean GSR of 672 ± 153 mm, resulting in a mean yield gap of 58%. The potential alfalfa WUE was 30 kg ha−1 mm−1 with associated minimum water losses of 24% of mean GSR, which was three times greater than the mean WUE of 10 kg ha−1 mm−1. The CIT suggested that GSR and minimum temperature in the season were the main yield-limiting weather variables in rainfed alfalfa production in the US. Our study also revealed that alfalfa was only limited by water availability in 21% of the environments. Thus, future research on management practices to narrow yield gaps at current levels of water supply is necessary.

Data fusion using climatology and seasonal climate forecasts improves estimates of Australian national wheat yields

National and regional yield forecasts can provide important insights into agribusiness beyond the farm gate. The incorporation of dynamical climate models into these forecasting systems strengthen their predictive performance in many cases but may contribute inherent biases to the final yield estimates. Downscaling the native climate model output so that is suitable for crop simulation modelling can also present challenges in representing realistic conditions for plant growth from a climate model. This study evaluated the performance of an operational national wheat yield forecast system for the Australian wheatbelt using climatology and seasonal climate model-based input data, and introduces an alternative approach using a data fusion method. The crop forecasting system uses the APSIM wheat model to estimate water-limited potential yield. The climatology-based forecast tended to over predict national yield (high yield bias; 1.5 to 7% across forecast months), while the model-based method (using ACCESS-S1 dynamical model) tended to under predict yield (low yield bias; -5.9 to -0.5% across forecast months) and had a lower spread than climatology (10 to 50% lower across forecast months). The model-based forecast had skill in terms of accuracy and reliability during the second half of the season. The newly developed data fusion method used a weighting method calibrated for separate forecast locations (stations) to remove bias in the mean forecast yield and reduce ensemble spread. This resulted in improvements in the Australia-wide yield forecasts across all forecast start dates. This study provides a demonstration of how a data-driven approach can be applied to a crop forecast to improve accuracy and resolution of crop yield forecasts without the need for more computationally intensive downscaling approaches.

Plastic film mulching affects field water balance components, grain yield, and water productivity of rainfed maize in the Loess Plateau, China: A synthetic analysis of multi-site observations

Plastic film mulching (PM) has been widely practiced for rainfed maize (Zea mays L.) in the Loess Plateau, China. However, controversy remains regarding its impacts on field water balance components. There is also scant quantitative information on water-limited potential grain yield (GYw) and water productivity (WPw). A meta-analysis and boundary line analysis were conducted using 1421 paired observations extracted from 83 peer-reviewed publications to (1) assess PM-induced changes in soil water storage at planting (SWSP), evapotranspiration (ET), and soil water storage at harvest (SWSH), and (2) quantify GYw and WPw in PM fields. PM significantly increased SWSP by 4.0% on average due to an 83.2% average increase in precipitation storage during the fallow season. Overall, ET showed a significant positive response to PM (+2.9%), resulting in 56.4% more soil water depletion than no-mulching (NM) fields over the growing season. However, SWSH significantly increased by an average 1.8% after applying PM, indicating that increased storage of fallow season precipitation replenished increased depletion of growing season soil water. Averaged across all samples, PM had significant positive effects on GY (+56.1%) and WP (+47.5%), which were attributed to higher transpiration, ratio of transpiration to ET, and harvest index from the perspective of effective crop water use. Based on GY and WP boundary lines, GYw and WPw under NM were estimated to be 10,728 kg ha-1 and 26.8 kg ha-1 mm-1 on average, respectively, and the respective averages under PM were 13,702 kg ha-1 and 34.8 kg ha-1 mm-1. The GY and WP gaps (i.e., differences between actual and potential values) averaged around 3300 kg ha-1 and 7.0 kg ha-1 mm-1, respectively, in NM and PM fields. In the GY versus ET and WP versus ET scatter plots, the vertical distances between the measured data points on a specific field and the corresponding boundary line indicated field-specific gaps for GY and WP. This study increases our understanding of the effect sizes of PM on field water balance components and the benchmarks of GY and WP for rainfed maize in the Chinese Loess Plateau and other regions with similar conditions worldwide.

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.

Assessing the impact of high-input management for reducing soybean yield gaps on high-productivity farms

To increase soybean actual yields (Ya), modern farmers are interested in exploring novel intensification options. We hypothesize that the inclusion of a high-input treatment in high-productivity systems may potentially reduce yield gaps (Yg), representing additional profits for farmers. Our objectives were to: (i) estimate Ya, water-limited yield potential (Ywlim), and Yg; (ii) evaluate a high-input treatment as a management option for reducing Yg; (iii) identify management and environmental factors associated to yield response to inputs and, (iv) determine the economic outcome of this high-input management on high-productivity soybean crops. We carried out 27 on-farm field experiments comparing a conventional management system versus a novel high-input treatment across the Central Pampas of Argentina. The conventional treatment was the most common management prescribed by farmers in the region. The high-input treatment included a set of inputs that were not usually applied and involved soil fertilizers with S and Zn, seeds treated with insecticides, fungicides, and inoculants, stress-reducing product, Ca and B foliar fertilizers and preventive insecticide. The Ywlim was estimated using crop simulation models. Yield gaps were calculated as the difference between Ywlim and Ya. Average Ya and Ywlim were 4571 and 5100 kg ha−1, respectively, with a Yg lower than 30%. High-input treatments promoted a yield response of 131 kg ha−1 across sites (p < 0.001), reducing Yg by 2.6%. Yield response to high-input treatment was positively correlated with Yg and soil pH, but negatively correlated with soil P. No positive economic outcomes were obtained with the high-input treatment. This study showed low benefits of the high-input treatments for reducing Yg in high-yielding soybeans managed by high-productivity farmers. Future yield increases in this type of systems would rely on genetic improvement of soybean yield potential and exploiting genotype x management x environment interactions.

Narrowing crop yield gaps in Ethiopia under current and future climate: A model-based exploration of intensification options and their trade-offs with the water balance

In the Central Rift Valley of Ethiopia (CRV), actual productivity of most cereals is less than 3 t ha −1 associated with low input use and poor crop management. After calibrating and validating the Agricultural Production System Simulator (APSIM) using experimental data, we explored and prioritized promising intensification options for rainfed maize and wheat that enable to narrow prevailing yield gaps in the CRV, and quantified trade-offs with the water balance and gross margins. We set up a factorial simulation experiment combining Genetic x Environment x Management factors that influence crop yield and water use at field scale to simulate yield and water balance components under current and future climate scenarios (pessimistic scenario for mid-century). Varietal selection and nitrogen (N) fertilization were the most important factors contributing to yield gap closure. Although yields were maximized with N application rates up to 250 kg −1 in most soils and varieties, maximum gross margin and maximum water use efficiency (WUE) were attained at lower N rates, associated with a small yield reduction compared to the maximum. There was a trade-off between intensification and increased absolute water use through transpiration, while the water use per kg product was decreased. However, location-specific N application rates that allow producing at least 80% of the water-limited potential yield (Yw) of maize and wheat resulted in high water use efficiencies as well as favorable cost-benefit ratios. Climate change was projected to lower yield as it advanced maturity, and to result in decreased drainage and increased soil evaporation across all variety, location and management combinations for both crops. Climate change reduced crop yield by 15–25% for wheat and 2–30% for maize. We conclude that the locally-calibrated APSIM model could be used to derive key lessons from the genetic, environment and management interactions, and generate information on sustainable intensification pathways that combine narrowing yield gaps with maximizing WUE and gross margins. • Variety selection and N fertilization contribute most to closing yield gaps (Yg) • Yg can be narrowed at N rates that maximize water use efficiency and gross margin • The potential for yield gap closure is crop, variety, soil and climate specific • Crop intensification increases total water use but decreases water per kg product • Climate change reduces land productivity by 15–25% for wheat and 2–30% for maize

Water use efficiency in Western Australian cropping systems

Rotations and associated management practices in rainfed farming systems of southwest Australia have shifted towards intensified cropping. Survey data from 184 fields spanning 14 Mha of southwest Australia were used to document water use efficiency (WUE) and water-limited yield potential (WLYP) of commercial crops and crop sequences and identify biophysical variables influencing WUE. WUE achieved in commercial wheat crops was 10.7 kg grain/ha.mm. Using a boundary function Ywl = 25 × (WU − 45), farmers achieved 54% of WLYP. Climate variables affected WUE more than management and biotic variates, the highest latitude region having WUE of 9.0 kg grain/ha.mm, compared to 11.8 kg grain/ha.mm for regions further south. Increased soil nitrogen and nitrogen fertiliser increased WUE, as did sowing earlier; in keeping with farmers in southern Australia sowing crops earlier and trebling fertiliser nitrogen usage since 1990. Wheat yield and WUE increased a small amount after break crop or pasture (12.5 kg grain/ha.mm) compared to wheat grown after wheat (11.2 kg grain/ha.mm), due to good weed and root pathogen control, and high fertiliser nitrogen application. However, WUE of wheat declined to 8.4 kg grain/ha.mm when more than three wheat crops were grown in succession. Farmers continue to improve WUE with increased inputs and new technologies replacing some traditional functions of break crops and pasture. However, break crops and pastures are still required within the rotation to maintain WUE and break effects need to be measured over several years.

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 data-rich analysis explains yield and quantifies yield gaps of winter wheat in the U.S. central Great Plains

With an annual production of ∼60 Mt, the U.S. accounts for about 8% of the global wheat (Triticum aestivum L.) production. Still, quantification of the yield gaps (YG) and major management factors to reduce it are scarce. We used Kansas, the largest wheat producing state in the U.S. located in the central Great Plains, for an initial assessment of on-farm yield and YG. We collected field-level management (37 variables), weather (8 variables), soil (two variables) and yield data from 656 commercial wheat fields over three harvest years (2016–2018) to (i) quantify management adoption levels, Ya, and YG, and (ii) identify interactions among management practices and weather variables using a data-rich approach. We also used our data as a case-study to detect whether differences in crop management among regions justified data clustering by crop zones. Water-limited yield potential (Yw) was simulated for each field-year using actual soil and weather data and the SSM-Wheat model. Fields were grouped in three climate zones based on their long-term climatology and important differences in cropping systems between zones. Grain yield averaged 3.8 Mg ha−1 and ranged from 0.3–7.1 Mg ha−1 across all regions and years. The YG averaged 44 %, with seasons with high Yw resulting in greater YG. Management practices most often associated with grain yield were management of nitrogen (N), phosphorus (P), and sulphur (S) fertilizer, as well as foliar fungicide and its interaction with variety reaction to major diseases, although these depended on in-season weather. Our analyses highlighted many other genotype × management × environment interactions explaining winter wheat Ya, such as regional-specific cultivar maturity and the dependency of sowing date (and its relation to seeding rate) on cropping system.

Sensitivity of Potato Yield and Biomass to Climate Change Effects in Gisozi, Burundi, and Washington, USA, and Assessment of LINTUL4 Model Behavior

Understanding climate change effects on crop production and evaluate the effectiveness of adaptation strategies in both developed and developing countries is of key importance. Crop simulation models can provide useful insight on the effects of increasing temperatures and rising CO2 concentrations [CO2] as well as rainfall variations. In this study, the LINTUL4 model was used to study the sensitivity effect of five temperature (T) levels (-3, 0, 3, 6, and 9oC above/below minimum/maximum temperatures), three precipitation (W) changes (30% decrease, baseline and 30% increase), and CO2 levels (baseline(360), 450, 540, 630 and 720ppm) on nutrient limited yield (Yn), water limited yield (Yw), water and nutrient limited yield (Ynw) and potential yield (Yp) of potato crop in high-input Washington, USA and low-input Gisozi, Burundi. The maximum weight of the tuber yield and aboveground biomass for Yp and Yw in Gisozi, and Yn and Yp in Washington was observed at combinations of lower temperature and elevated [CO2]. For Gisozi, maximum tuber yield for Yn and Ynw was observed at [CO2] of less than 720ppm. The results suggest that nutrient supply will continue to be the major limiting factor for potato production under elevated [CO2] in Gisozi, and water availability will limit Yw and Ynw rain-fed production in Washington. Generally, the LINTUL4 model performs well with few data input, but fails to predict the differential effect of high temperature on assimilate partitioning to aboveground and belowground biomass.