Average Yield Gap Research Articles

Yield variation and yield potential of organic arable crops in Finland derived from statistical data

This study aims to expand and diversify the analysis of crop yields in organic production by utilising the long-term statistical data reported by farmers. We examined the variation of the harvested yields of organic arable crops (season’s ad hoc harvest) on Finnish organic farms and determined the yield potential for different organic crops and in comparison, with conventional counterparts. When comparing median yields per ha, organic yields were on average 65% of conventional yields. The mean yield difference significantly differed between plant species. Among the analysed crops, faba bean performed best, gaining on average 84% of conventional yields, while organic cereals attained an average of 54–68% of conventional yields. The ratio of organic and conventional median yields was compared between geographical regions, and significant regional differences were found. The performance of organic cereals remained stable over the years, but the relative performance of the organic grain legumes, pea and faba bean, depended more on growing conditions. An average yield gap between the best-performing and medianfarms was 38% for organic farms and 28% for conventional farms. This indicates that Finnish organic farms have greater potential to improve the yield level than conventional farms.

Influence of Front-Line Demonstrations on Yield, Yield Gap and Economics of Field Pea under Hill Region of Arunachal Pradesh, India

Krishi Vigyan Kendra Yachuli, Lower Subansiri district, gave a front-line field pea demonstration in 06 villages across two blocks with 44 farmers during the 2019-20 and 2020-21 seasons. FLD on the VL Matar 42 variety of field pea was carried out over the course of two years in a 15 ha area using the recommended improved practices. Additionally, a control plot with farmer practices was maintained. The yield in the farmers' plot (1110 kg ha-1) and demonstration plot (1510 kg ha-1) in the year 2020-21 was higher than in the year 2019-20. In the years 2019-20 and 2020-21, respectively, the demonstration plots' mean yield exceeded that of the farmers' plot by 35.23 and 36.03 percent. The VL Matar 42 variety of field pea had a mean yield of 1465 kg ha-1, which was lower than the potential yield of 1868 kg ha-1. The yield gap of 403 kg ha-1 indicates that there is a technology gap. Interestingly, the average extension yield gap was lower (385 kg ha-1) during the study period. The technology index varied from 19.16 to 23.98 percent, showing the feasibility of the evolved technology at the farmer's fields. Cultivating field pea using improved technologies resulted in an average higher net return of Rs. 59,050 ha-1 compared to Rs 34,500 ha-1 from local farming practices. The benefit cost ratio of field pea was higher (2.16) when using improved technologies compared to (1.83) when using farmers' practices.

Evaluation of Integrated Pest Management Module against Pests of Ground Nut and Productivity, Profitability Analysis under Open Field Conditions

Background: Groundnut is an annual oilseed crop that is cultivated in many tropical and sub-tropical countries for its seed purpose. The productivity of groundnut has drastically reduced since 2015 due to an array of insect pests especially sucking pests have emerged as a prominent factor contributing to decreased yields in groundnut by direct damage as well as vectors of virus diseases. Methods: The integrated package of practices including seed treatment with fungicide (mancozeb @ 3 g/kg seed) followed by insecticide (imidacloprid @ 2 mL/kg seed) and new generation insecticides (imidacloprid, chlorpyriphos and flubendiamide) were evaluated against sucking pests (aphid, leaf hopper and thrips) and leaf feeders (Spodoptera litura) under open field conditions in scarce rainfall zone of Andhra Pradesh, India during 2020-21, 2021-22 and 2022-23. The parameters regarding incidence of pests, population reduction after treatment imposition, safety to natural enemies, phytotoxicity effects, extension gap, technology gap and technology index, were worked out in farmer’s field and demonstration field. Result: In this present study imidacloprid 17.8 SL was performed well in controlling aphids, leaf hoppers and thrips in all the three seasons and flubendiamide 39.35 SC was concluded as best treatment against Spodoptera litura in all seasons. During all the study years, the benefic cost ratio of demo practice indicates (1.26, 1.01 and 1.04) when compared to farmers practice (0.71, 0.69 and 0.76), respectively for 2020, 2021 and 2022. Similarly, the average extension gap, technology gap and yield gap were worked out and ranged from (3.4, 3.1 and 5.3), (1.82, 4.23 and 1.82), (12.72, 11.36 and 19.46) for the years 2020, 2021 and 2022, respectively.

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.

Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources

Improved agricultural production systems, together with increased grain yield, are essential to feed the growing global population in the 21st century. Global gridded crop models (GGCMs) have been extensively used to assess crop production and yield simulation on a large geographical scale. However, GGCMs are less effective when they are used on a finer scale, significantly limiting the precision in capturing the yearly maize yield. To address this issue, we propose a relatively more advanced approach that downsizes GGCMs by combining machine learning and crop modeling to enhance the accuracy of maize yield simulations on a regional scale. In this study, we combined the random forest algorithm with multiple data sources, trained the algorithm on low-resolution maize yield simulations from GGCMs, and applied it to a finer spatial resolution on a regional scale in China. We evaluated the performance of the eight GGCMs by utilizing a total of 1046 county-level maize yield data available over a 30-year period (1980–2010). Our findings reveal that the downscaled models created for maize yield simulations exhibited a remarkable level of accuracy (R2 ≥ 0.9, MAE < 0.5 t/ha, RMSE < 0.75 t/ha). The original GGCMs performed poorly in simulating county-level maize yields in China, and the improved GGCMs in our study captured an additional 17% variability in the county-level maize yields in China. Additionally, by optimizing nitrogen management strategies, we identified an average maize yield gap at the county level in China ranging from 0.47 to 1.82 t/ha, with the south maize region exhibiting the highest yield gap. Our study demonstrates the high effectiveness of machine learning methods for the spatial downscaling of crop models, significantly improving GGCMs’ performance in county-level maize yield simulations.

Identifying yield and growing season precipitation gaps for maize and millet in Cameroon

Abstract Climate change drives huge differences between the actual and projected yield and growing season precipitation. Therefore, this work identifies yield and precipitation gaps for maize and millet at the national and subnational scales as well as policy considerations for agricultural policy experts that can mitigate these gaps. Yield data for the national and subnational scale analyses were obtained for the period 1961–2021 from the FAOSTAT and the Ministry of Agriculture (MINAGRI)/IRAD of Cameroon, respectively. Growing season precipitation data for the national and subnational scales were collected from the World Bank climate change portal and the Climate Research Unit (CRU). Various machine learning algorithms were used to bias-adjust the data and to compute the potential yield and growing season precipitation from which the yield and precipitation gaps were computed. The results show a positive correlation between yield and precipitation gaps, with millet depicting the strongest correlation. The average yield gap for maize is 0.55 t/ha, higher than the average yield gap for millet that is 0.28 t/ha. Not all years with yield gaps are correlated with precipitation gaps. The average precipitation gap for maize is 108 mm/year, and it is higher than the 101 mm/year recorded for millet.

Yield gap variation in rice cultivation in Indonesia

Abstract The rice yield gap (YG) is a global concern, requiring more detailed studies spatially and temporally. As a staple food in Indonesia, rice was produced from 7.4 Mha paddy fields in 2019. Better insight into the YG helps assess measures to boost rice production. However, the information on YG variation among regions scale is limited. This study aimed to identify the rice YG based on 295 historical trial datasets from 23 provinces in Indonesia. We surveyed published trial results from 2012 to 2022 and analyzed YGs, expressed as the percentage of farmer yield (FY). The potential yield (PY) was estimated from field trial results using introduced rice cultivation technology package, whereas FY from results using existing farmer practices. Our study showed that the average YG was 62% in rainfed, 54% in tidal, and 32% in irrigated paddy fields. The YG was significantly high in the paddy fields of Kalimantan (74%) and Maluku-Papua (49%), while the lowest was in Sulawesi (27%) and Java (31%). The YG varied significantly with geo-regions, rice varieties, and cultivation technology packages. Closing the YG and ensuring sustainable rice production requires the implementation of sustainable intensification through applying site-specific technology packages, reallocation of agricultural interventions to a higher YG region, and rice variety improvement to increase PY.

Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches.

This study aimed to identify the most influential soil and environmental factors for predicting wheat yield (WY) in a part of irrigated croplands in southwest Iran, using the FAO-Agro-Climate method and machine learning algorithms (MLAs). A total of 60 soil samples and wheat grain (1m × 1m) in 1200ha of Pasargad plain were collected and analyzed in the laboratory. Attainable WY was assessed using the FAO method for the area. Pearson correlation analysis was used to select the best set of soil properties for modeling. Topographic attributes and vegetation indices were used as proxies of landscape components and cover crop to map actual WY in the study area. Two well-known MLAs, random forest (RF) and artificial neural networks (ANNs), were utilized to prepare an actual continuous WY map. The k-fold method was used to determine the uncertainty of WY prediction and quantify the quality of prediction accuracy. Results showed that soil organic carbon (SOC) and total nitrogen (TN) had a positive and significant correlation with WY. The SOC, TN, normalized different vegetation index (NDVI), and channel network base level (CHN) were recognized as the most important predictors and justifying more than 50% of actual WY. The ANNs outperformed the RF algorithm with an R 2 of 0.75, RMSE of 400 (kg ha-1), and RPD of 2.79, according to statistical indices. The uncertainty analysis showed that the maximum uncertainty of the prediction map [400 (kg ha-1)] was very low compared to the mean value [4937 (kg ha-1)] of WY map. Calculation yield gap using the FAO-agro-climatic model showed that the average yield gap of the region was about 50% of actual yield. The findings of this study demonstrated that integrating simulated attainable crop growth using crop model and a set of soil and environmental covariates with the ANNs algorithm can effectively predict WY gaps in large areas with acceptable and reasonable accuracy. The study emphasizes that the implementation of efficient management practices has the potential to enhance agricultural production in the study area and similar regions. These results represent a significant advancement of sustainable agriculture and provide valuable insights for ensuring global food security.

Pesticide-free arable cropping systems: performances, learnings, and technical lock-ins from a French long-term field trial

To ensure regular and high yields, current agriculture is based on intensive use of pesticides and fertilizers, which are detrimental to the environment and human health. Moreover, as pest resistance to pesticides increases, and more and more pesticides are taken off the market, national and European policies are becoming powerful drivers to deliver pesticide-free farming systems. Whereas numerous studies have compared organic versus conventional systems, our study assessed, for the first time, the performances of a pesticide-free arable cropping system (No-Pesticide), using synthetic fertilizers, specifically designed to produce high yields and meet environmental goals. This system was compared with an input-based cropping system designed with the same environmental targets (PHEP: productive with high environmental performances) in an 11-year field trial in France (Paris Basin). Banning pesticides did not result in a significant average yield gap (in GJ.ha−1.year−1 or in kg N.ha−1.year−1) calculated over the crop sequence. Yet, some crops’ yields significantly decreased, due either to pest damages, or to limited nitrogen nutrition. In the No-Pesticide system, the mycotoxin content of cereal grains was lower than the regulatory threshold, and the average wheat protein content was higher than the required standard for baking. Indirect energy consumption, total greenhouse gas emissions, number of technical operations, nitrogen fertilizer amounts, and treatment frequency indexes were significantly lower compared to the PHEP system. Conversely, results showed significantly higher direct energy consumption, direct greenhouse gas emissions, and number of work hours for weed control. We identify highly effective agricultural strategies to avoid pesticide use (e.g., widely diverse and long crop sequence; introduction of hemp) and pinpoint several technical lock-ins hampering steady production in pesticide-free systems. We argue that more experiments should be undertaken to deliver technical knowledge for managing major or orphan species within pesticide-free systems, and to provide supplementary results, including economic and social performances.

Assessment of farmers’ knowledge and perceptions of coffee yield reduction due to weeds and their management in Ethiopia

The home of Coffea arabica is in Ethiopia, where it has high genetic diversity and suitable growing conditions; unfortunately, the national average yields of coffee remain low due to no technical advancements and diverse, complex biotic and abiotic constraints. Hence, this study was conducted in eight major coffee-growing zones of Ethiopia to assess farmers' knowledge and perceptions of coffee yield reduction due to weeds and the farmers' weed control practices. A purposive and random sampling technique was used to generate primary data from coffee growers (N = 320) using a semi-structured questionnaire. Quantitative data were analyzed using a three-stage nested design, and the dependent and independent variables data were subjected to canonical correlation analysis. This study revealed variation in coffee yield (t ha−1) among the assessed areas based on farmers’ knowledge of estimating coffee yield. The average yield level ha−1 was very low (0.37 t ha−1) and different among the surveyed areas. The average coffee yield gap as compared to the current national level (0.64 t ha−1) was observed to be 42%, and this low yield was highly correlated with weed infestation (r = 0.879) and type of weeds r = −0.528). This investigation indicated a single factor or association of different factors contributing to the low yield level of coffee in the study areas. Thus, it is concluded that predictor variables accounting for the low yield levels need to be considered when planning future strategies to attain the yield potential of C. arabica in Ethiopia.

Integrating SAR Sentinel-1A and DSSAT CROPGRO Simulation Model for Peanut Yield Gap Analysis

Crop yield data are critical for managing agricultural sustainability and assessing national food security. This study aims at increasing peanut productivity from its current levels by analyzing the yield gap (difference) of potential production between theoretical yield and actual farmers’ yields. The spatial yield gap of peanut for the Tiruvannamalai district of Tamil Nadu is examined in this investigation by integrating the products of microwave remote sensing (SAR Sentinel-1A) with the DSSAT CROPGRO Peanut simulation model. The CROPGRO (crop growth) Peanut model was calibrated and validated by conducting a field experiment at Oilseeds Research Station, Tindivanam during Rabi (spring) 2019 for predominant cultivars, i.e., TMV 7, TMV 13, VRI 2 and G 7. Actual attainable yield was recorded by organizing crop cutting experiments (CCEs) with the help of the Department of Agriculture Economics and Statistics in the respective monitoring villages. The regression analysis between the maximum recorded DSSAT leaf area index (LAI) at the peak flowering stage of peanut and the yield recorded by CCEs for the spatial yield estimation of peanut in the Tiruvannamalai district of Tamil Nadu during Rabi 2021 was carried out using ArcGIS 10.6 software. The DSSAT CROPGRO simulated potential yield ranged from 3194 to 4843 kg/ha, whereas actual yield ranged from 1228 to 3106 kg/ha, with a considerable disparity between the actual and potential yield levels (from 1217 to 2346 kg/ha) of the monitored locations. The minimum, maximum and average yield gaps in peanut for Tiruvannamalai district were assessed as 1890, 2324 and 2134 kg/ha, respectively. In order to reduce the production difference of peanut cultivation, farmers should focus more on management issues such as time of sowing, irrigation or water management, quantity and sources of nutrients, cultivar selection and availability of quality seeds tailored to each region.

Identification of weak links in production technology for bridging the canola yield-gap in Punjab, Pakistan

AbstractUnderstanding the reasons for the yield gap between potential and actual yield can provide insights for enhancing canola production by adapting measures for ensuring food security. The canola yield gap under different management practices (e.g. water, nitrogen, N- and sowing dates) was quantified using research trials that were conducted at on-station and historical data (1980–2016) and the CROPGRO-Canola model for Punjab, Pakistan. The integrated approach revealed that low inputs of N, the amount of irrigation, sowing date and the use of seeds from home stocks were the principal causes for a low yield. The CROPGRO-Canola model was able to simulate the canola yield from research trials (R2 = >0.90) and farm survey data (R2 = 0.63). The average yield gap between potential (YP), N-limited (YNL), water-limited (YWL), N- and water-limited (YNWL), and overall farmer field yield (YOFF) was 50, 46, 62 and 72%, respectively. The yield-gap with achievable yield (YA) for YNL, YWL, YNWL and YOFF was 34, 28, 49 and 63%, respectively. Overall, the results showed that a high canola yield for farmers’ fields can be obtained by selecting appropriate varieties and sowing dates with N rate of 120 kg/ha and efficient irrigation management. However, further studies are necessary to fully comprehend the underlying causes for the low actual yield and the high yield variability of farmers’ fields.

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.

Model-Based Yield Gap Assessment in Nepal’s Diverse Agricultural Landscape

Rice, wheat, maize, millet, and barley are the five major staple cereal crops in Nepal. However, their yields are low, and imports are needed to meet domestic demand. In this study, we quantify the gap between current and potentially attainable yields in Nepal, estimate how much additional fertilizer and irrigation are required to close the gap, and assess if self-sufficiency can thus be achieved. For this, we first test the ability of the crop model EPIC to reproduce reported yields in 1999–2014 accurately. On average, simulated and reported yields at the national level were in the same range, but at the district level, the error was large, as the resolutions of the available climate and soil input data were not high enough to depict the heterogenic conditions in Nepal adequately. In the main study, we show that average yield gaps in Nepal amount to 3.0 t/ha (wheat), 2.7 t/ha (rice), 2.9 t/ha (maize), 0.4 t/ha (barley), and 0.5 t/ha (millet). With additional irrigation and fertilization, yields can be increased by 0.1/2.3 t/ha (wheat), 0.4/1.3 t/ha (rice), 1.6/1.9 t/ha (maize), 0.1/0.3 t/ha (barley), and 0.1/0.4 t/ha (millet), respectively. The results show that providing reliable and affordable access to fertilizer should be a priority for closing yield gaps in Nepal.

Modeling Potential production and yield gap of potato using modelling and GIS approaches

Understanding yield potential (Yp) and yield gap (Yg) in current intensive potato (solanum tuberosum L.) production is essential to meet future food demand with the limited resources. Evaluating yield gap is a strong approach to estimate maximum production potential when all factors are in the best condition. A complete estimation of yield gap and potential yield across all major potato producing regions in Iran is lacking. The global yield gap atlas (GYGA) protocol was used to estimate potential yield of potato in Iran. This protocol is based on the climatic zones (CZs) and the reference weather stations (RWS) buffer zones, soil types in each buffer zone. Thirty-five RWS buffer zones in potato producing regions were selected, and total potato area in the RWS buffer zones covered 83% of the whole potato harvest area. According to the results, the average Yp was 67.3 t ha–1 and actual yield (Ya) was 30 t ha–1. Therefore, the average tuber yield gap was 37.3 t ha–1. These results indicate that the Potato producers achieved 45% of the potential yield in Iran. Iranian farmers produced 5 million tons of potato from about 164,000 ha. If they can obtain only 80% of Yp (53.8 t ha–1), amount of potato production will be 8.8 million tones. As result, they can produce 5.2 million tons tuber yield of potato in 97,000 ha cultivation area. Thus, with closing yield gap and increasing potato production, it is possible to decrease potato lands.

Analysis of Levels and Determinants of Technical Efficiency of Teff Producing Farmers: The Case of East Gojam Zone, Amhara Region, Ethiopia

This study explores Technical Efficiency level of smallholder teff production and also investigates socioeconomic determinants explaining the variation in Technical Efficiency among smallholder teff farmers using 396 random sample farm households in East Gojam Zone of Amhara regional state, Ethiopia. Cobb-Douglas Stochastic Frontier Model was fitted to estimate farm level technical efficiency and the associated efficiency scores since the function has been one of the most widely used models among the potential algebraic forms of production function in most empirical studies of agricultural production analysis. Both Maximum Likelihood (ML) and quantile regression (QR) techniques were employed to conduct the analysis of technical efficiency determinants. And the study revealed that a one % increase in the input level of plot size, seed, fertilizer, man-days and oxendays respectively produce a return of 0.30, 0.11, 0.02, 0.28 and 0.14% increase in output, keeping all other factors constant. In general, the result had underlined farmer’s opportunity to boost their productivity by optimizing the level of the inputs they are using. On this study the mean levels of the actual and potential output during the production year were 14.00 qt/ha and 21.07 qt/ha and, hence, with 7.07Qt/ha yield gap. Therefore, the ratio of the average yield gap to the average actual yield entails the possibility to increase the average plot level output by 50.5% through improving producers TE under the prevailing production input and technology level. The study also identified that farmers with more fertile plots are technically efficient as compared to those who cultivate marginal lands, keeping all other factors constant. Besides, those teff farmers who use compost have lower inefficiency as compared to those farmers who do not utilize compost in the study area. Moreover, education level influenced efficiency of teff farmers positively. Therefore, the study recommends the concerned bodies to give emphasis to promote education, and to have integrated soil fertility management policy targeting soil fertility enhancement to move the marginalized smallholder farmers TE closer to the frontier. Keywords: Technical efficiency, Stochastic frontier model, Quantile regression, Teff, Amhara, Ethiopia DOI: 10.7176/JESD/13-9-02 Publication date: May 31 st 2022

Dynamics and Determinants of the Grain Yield Gap in Major Grain-Producing Areas: A Case Study in Hunan Province, China.

Understanding the dynamics of the grain yield gap (YGAP) and its causative factors is essential for optimizing the layout of grain production and addressing the food crisis, especially in countries with a huge population and less cultivated land, such as China. In the study, a spatial analysis- and machine learning-based framework for YGAP analysis was developed, taking Hunan Province, China, as an application. The results showed that the average YGAP in Hunan Province gradually narrowed from 1990 to 2018, and the YGAPs narrowed in 116 counties. Of which, 26 counties narrowed by more than 4 t ha−1, 58 counties narrowed from 2–4 t ha−1, and 32 counties narrowed within 2 t ha−1. Additionally, we found that the GDP per capita (GDPPC), sunshine hours (SH), per capita annual net income of farmers (PCAI), and rural electricity consumption (REC) play a key role in YGAP change, and the importance of human investment to the YGAP decreased, while socioeconomic environment became the dominant factor that influenced grain production. Comprehensively, the relatively great potential for grain yield growth was generated in sixty-four counties, which are mainly located in the northern, central, and southern Hunan. The findings suggest that it is necessary to consider the trends of economic development in rural areas and population migration in agricultural management. This work provides insights into yield gap dynamics and may contribute to sustainable agricultural management in Hunan Province, China, and other similar regions.

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.

Southeast Asia must narrow down the yield gap to continue to be a major rice bowl

Southeast Asia is a major rice-producing region with a high level of internal consumption and accounting for 40% of global rice exports. Limited land resources, climate change and yield stagnation during recent years have once again raised concerns about the capacity of the region to remain as a large net exporter. Here we use a modelling approach to map rice yield gaps and assess production potential and net exports by 2040. We find that the average yield gap represents 48% of the yield potential estimate for the region, but there are substantial differences among countries. Exploitable yield gaps are relatively large in Cambodia, Myanmar, Philippines and Thailand but comparably smaller in Indonesia and Vietnam. Continuation of current yield trends will not allow Indonesia and Philippines to meet their domestic rice demand. In contrast, closing the exploitable yield gap by half would drastically reduce the need for rice imports with an aggregated annual rice surplus of 54 million tons available for export. Our study provides insights for increasing regional production on existing cropland by narrowing existing yield gaps.

Optimizing nitrogen fertilizer inputs and plant populations for greener wheat production with high yields and high efficiency in dryland areas

Bridging yield gaps in staple crops is widely reported as a highly efficient means for improving grain production. However, changes in production efficiency and the environmental footprint remain less investigated when narrowing these gaps. In this study, we collected data from 299 wheat plots on the Loess Plateau of China to identify changes in nitrogen efficiency, nutrient balance, and environmental risks when narrowing the yield gaps. The low-yielding (LY) and high-yielding (HY) plots had an average yield gap of 1936 kg ha–1 (33.5%) and nitrogen partial factor productivity gap of 10.3 kg kg–1 (30.1%) that were simultaneously narrowed under similar fertilizer inputs. Optimizing sowing date improved spikes per m2 (27.4%) and grains per head (12.9%), key measures to improving wheat grain yields. The high-yielding high-efficiency (HYHE) plots produced on average 43.0% and 14.6% higher nitrogen partial factor productivity and grain yield, respectively than the high-yielding low-efficiency (HYLE) plots. Appropriate nitrogen fertilizer inputs significantly enhanced nitrogen use efficiency and maintained grain yield on HY plots. Grain yield and nitrogen partial factor productivity gradually increased with year of cultivar release (1995–2019) (P < 0.05). However, soil organic matter, pH, available phosphorus, mineral nitrogen, and available potassium had no significant effect on grain yield or nitrogen use efficiency. The HYHE plots had the lowest nitrogen surplus (25 kg N ha–1), phosphorus surplus (31 kg P ha–1), and greenhouse gas emissions (2327 kg CO2-eq ha–1) of the surveyed plots. In the HY plots, moderate potassium fertilizer should be supplemented to balance wheat production. In conclusion, excellent cultivars and high plant density are key for bridging the yield gap between LY and HY plots. Optimizing fertilizer management could further promote HY plots from low to high production efficiency. This research provides farmers with the knowledge and methods to sustainably bridge field-level yield gaps in dryland wheat production.