Wheat Yield Gap Research Articles

Winter Wheat Historic Trends and Yield Gaps in the Wheat State

AbstractWheat is the most widely grown grain crop in the world, and the United States is ranked as the fourth major wheat producer after China, India, and Russia. Kansas is the top winter wheat‐producing state in the United States and, as such, is nicknamed “The Wheat State.” A significant yield gap has existed in Kansas between potential winter wheat yields and the actual farm level average yield. Due to current conflict in the major wheat‐growing region of Ukraine and Russia and recent droughts, record decline in world wheat stocks was expected. To further understand winter wheat’s contribution to the economy, production trends, and relationship with climate variability, data available in the state of Kansas through annual winter wheat yield trials and USDA reports were used to study winter wheat historic trends and relations. Earn 0.5 CEUs in Crop Management by taking the quiz for the article at https://web.sciencesocieties.org/Learning‐Center/Courses.

Estimating Yield Gap of Wheat (Triticum aestivum L.) using the Comparative Performance Analysis (CPA) Method in the Bam, Narmashir and Fahraj Counties of Kerman Province

Background: Yield gap is distance between potential yield and attainable yield (yield gap= potential yield-attainable yield) in order to increase the crop yield and the goal of sustainable agriculture. Methods: This study was conducted in Bam, Narmashir and Fahraj counties in 219 separate farms (79 farms in Bam, 72 farms in Narmashir and 68 farms in Fahraj) during the years 2019 and 2020 to investigate the yield gap of wheat and determine the limiting factors and their contributions to yield reduction, using the comparative performance analysis (CPA) method. All information related to soil factors, management factors, climatic factors and agronomic traits (53 variables) were measured and recorded. The relationship between wheat yield and all variables was examined using a stepwise multiple regression method. A production model was determined for soil and management factors. Result: The results showed a yield gap between actual yield and potential yield in the regions of Bam, Narmashir and Fahraj, which were 1770.8, 1817.61 and 1605 kg per hectare, respectively. Since the actual yield in these regions was 4248, 4228 and 3161 kg per hectare, the yield gap was observed to be 33.6%, 30% and 29.4%, respectively. The effective factors in reducing the yield in the Bam region included water shortage (19.48%), nitrogen deficiency (16.09%), delayed planting (15.40%), soil available phosphorus deficiency (10.14%), weed density (10.8%), soil organic carbon (13.59%) and inappropriate plant density (14.47%). In the Narmashir region, water shortage (26.68%), nitrogen deficiency (18.86%), soil organic carbon (11.78%), inappropriate plant density (12.43%), wheat aphid (19.58%) and weed density (10.64%) caused yield reduction. In the Fahraj region, water shortage (20.65%), nitrogen deficiency (19.62%), delayed planting (9.44%), soil available phosphorus deficiency (8.45%), weed density (12%), soil salinity (12.80%) and black stem disease (17%) caused a decrease in wheat yield.

Predicting wheat yield gap and its determinants combining remote sensing, machine learning, and survey approaches in rainfed Mediterranean regions of Morocco

Wheat plays a crucial role in Morocco’s food security, economic stability, and livelihoods of farming communities. Assessing key vegetation indices (as yield predictors), along with understanding potential yield, yield gap, and major determinants for this gap at regional and national scales, is vital for improving food security with resilience in variable climatic conditions. Analysing the yield gap and its causes during drought and optimal weather conditions can reduce crop failure risks and enhance productivity specially in variable rainfed production systems. This study aimed to develop scalable methodology to predict field- and landscape-level yield and yield gaps for wheat and their underlying causes examplifying Morocco’s rainfed production environment combining remote sensing, machine learning, and ground information. By analysing six vegetation indices (EVI2, CGVI, MSR, NDVI, OSAVI, and RVI) derived from Sentinel-2 satellite imagery (10 m resolution) over three successive growing seasons (2018–2019, 2019–2020, and 2020–2021), the study employed advanced vegetation index models for accurate prediction of wheat yields and yield gaps at plot and on a larger regional scale within the Rabat-Sale-Kenitra region. To identify the determinants of yield gap, climate and soil datasets were merged with crop management information and the random forest model was fine-tuned and assessed for each season and cumulatively. The findings highlighted that RVI, GCVI, and NDVI vegetation indices were particularly effective in predicting wheat yields, showing the highest R2 and the lowest prediction errors (RMSE). Such predictive methodologies are crucial for policymakers to proactively plan and mitigate risk minimization and adaption plans at regional and national levels. The models predicted rainfed potential yields of 5.99, 1.53, and 4.66 t ha−1, with corresponding yield gap of 3.38, 0.73, and 1.58 t ha−1 for the seasons of 2018/2019 (favorable); 2019/2020 (drought) and 2020/2021 (favorable), respectively. Across three periods, critical factors determining yield include soil moisture, total rainfall during the crop growing period, evapotranspiration, and soil texture and carbon content. To minimize drought risks and maximize benefits during variable rainfall conditions, it is essential to implement pre-season drought forecasts, customize seeding dates based on soil moisture, adopt technologies that enhance soil moisture retention, and utilize climate-adapted farming practices in the semi-arid and arid rainfed regions.

Wheat (Triticum aestivum) yield gap affected by soil physicochemical properties

Wheat (Triticum aestivum) yield gap affected by soil physicochemical properties

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.

Yield and Production Gaps in Wheat in Parsabad Moghan, northwest of Iran

Yield and Production Gaps in Wheat in Parsabad Moghan, northwest of Iran

Opportunities to close wheat yield gaps in Nepal's Terai: Insights from field surveys, on-farm experiments, and simulation modeling

CONTEXTWheat (Triticum aestivum) is among the most important staple food crops in the lowland Terai region of Nepal. However, national production has not matched the increasing demand. From a South Asian regional perspective, average productivity is low with high spatial and temporal variability. OBJECTIVESThis study determines entry points for closing yield gaps using multiple diagnostic approaches, i.e., field surveys, on-farm experiments, and simulation models across different wheat production environments in the Terai region of Nepal. METHODOLOGYYield and production practice data were collected from 1745 wheat farmers' fields and analysed in tandem with over 100 on-farm experiments. These were complemented by long-term simulation modeling using the APSIM Next Generation to assess system production behavior over a range of climate years. RESULTS AND DISCUSSIONOn-farm survey data suggests that yield and profit gaps under farmers' management (difference between the most productive (top 10th decile) and average wheat fields) were 1.60 t ha−1 and 348 USD ha−1 in the Terai region. The potential yield gap (difference between simulated potential yield and surveyed population mean) estimated was 4.63 t ha−1, suggesting ample room for growth even for the highest-yielding fields. Machine learning diagnostics of survey data, and on-farm trials identified nitrogen rate, irrigation management, terminal heat stress, use of improved varieties, seeding date, seeding method, and seeding rate as the principal agronomic drivers of wheat yield. While fields in the top 10th decile yield distribution had higher fertilizer use efficiencies and irrigation and seeding rates with similar overall production costs as average-yielding farmers. Our results suggest a complementary set of agronomic interventions to increase wheat productivity among lower-yielding farms in the Terai including advancing the time of seeding by 7–10 days on average, increasing nitrogen fertilizer by 20 kg ha−1, and alleviating water stress by applying two additional irrigations. SIGNIFICANCEAlthough wheat yields in the Terai are among the lowest in the region, biophysical production potential is high and remains largely untapped due to sub-optimal agronomic management practices rather than intrinsic agroecological factors. Data from this study suggests that incremental changes in these practices may result in substantial gains in productivity and profitability.

Current State and Limiting Factors of Wheat Yield at the Farm Level in Hubei Province

Longitudinal wheat yields in China have declined in recent times due to climate change, more frequent natural disasters, and suboptimal agronomic management. To date, it has been unclear which factors have predominated yield penalties realised hitherto in Hubei Province. This study aimed to identify key factors limiting wheat production across systems and agroecological regions, and provide a basis for increasing crop production while engendering food security. Survey data from 791 households in Hubei Province were analyzed using descriptive statistics and logistic regression. Significant spatial heterogeneity in average wheat yields was observed, with the Jianghan Plain region having significantly lower yields compared with the northwest region (yield gap: 1125 kg·hm−2). Dryland wheat had higher average yields than rice-rotation wheat (yield gap: 134 to 575 kg·hm−2). Socioeconomic factors, cultivation management measures, and environmental factors contributed differently to yield differences. Input costs and economic benefits were key social factors influencing wheat production. Variation in management were mainly attributed to planting methods, while soil fertility and climatic factors limited yields in some regions. In the northwest, low soil fertility and susceptibility to drought and high temperatures had greater influence on yields. In the Jianghan Plain, soil waterlogging and erosion were key challenges. Waterlogging increased the probability of low yields by 8.6 times, while severe soil erosion increased probability of yield loss by a factor of almost five. Low-yield farms in the Jianghan Plain were 21% higher than those in the northwest. Extreme weather events also contributed to low yields in the Jianghan Plain. We note significant potential for increasing farm-level wheat production in Hubei Province, with large existing differences across agro-ecological regions and planting modes. Differences in cultivation practices was a major driving factor of yield gaps between planting modes, while soil fertility and meteorological disasters drive regional yield differences. These results have implications for those aspiring to narrow the yield gap across regions and increase production of cereal crops.

Modelling of the Factors Creating the Yield Gap of Wheat in the Shushtar Area

Modelling of the Factors Creating the Yield Gap of Wheat in the Shushtar Area

Adverse Weather Impacts on Winter Wheat, Maize and Potato Yield Gaps in northern Belgium

Adverse weather conditions greatly reduce crop yields, leading to economic losses and lower food availability. The characterization of adverse weather and the quantification of their potential impact on arable farming is necessary to advise farmers on feasible and effective adaptation strategies and to support decision making in the agriculture sector. This research aims to analyze the impact of adverse weather on the yield of winter wheat, grain maize and late potato using a yield gap approach. A time-series analysis was performed to identify the relationship between (agro-)meteorological indicators and crop yields and yield gaps in Flanders (northern Belgium) based on 10 years of field trial and weather data. Indicators were calculated for different crop growth stages and multiple soils. Indicators related to high temperature, water deficit and water excess were analyzed, as the occurrence frequency and intensity of these weather events will most likely increase by 2030–2050. The concept of “yield gap” was used to analyze the effects of adverse weather in relation to other yield-reducing factors such as suboptimal management practices. Winter wheat preferred higher temperatures during grain filling and was negatively affected by wet conditions throughout the growing season. Maize was especially vulnerable to drought throughout the growing season. Potato was more affected by heat and drought stress during tuber bulking and by waterlogging during the early growth stages. The impact of adverse weather on crop yield was influenced by soil type, and optimal management practices mitigated the impact of adverse weather.

Wheat Yield Gap Assessment in Using the Comparative Performance Analysis (CPA)

One of the crucial issues in developing nations is diminishing the yield gaps. Therefore, accurate yield gap estimation has many real-world uses for increasing crop production. Utilizing comparative performance analysis (CPA) techniques, the yield gap of wheat fields was evaluated in this study. In Varamin, Tehran Province, Iran, data on 104 wheat fields were collected between 2018 and 2020 and every aspect of wheat field management has been documented. The CPA model determines the yield gap’s contributing factors and potential yield. The results of data analysis revealed that the production ranged from 2600 to 7600 kg ha−1. The CPA method predicted a potential yield of 9316 kg ha−1 and found a yield gap of 3748 kg ha−1; this amount was 40.23% of the potential yield. Leaf chlorophyll (29%), irrigation at stem extension (9%), LAI (7.7%), soil salinity (8.2%), field area (16.3%), phosphorus consumption (6%), nitrogen utilized at the stage of tillering (16%), and HI (7.8%) all contributed to the yield gap in the CPA. It has been said that the computed yield in CPA is a potential yield that can be reached. CPA is a cheap and straightforward tool that could identify yield gaps and their causes in a district without the need for costly experiments. Therefore, developing nations with significant efficiency and yield gaps can use these techniques effectively.

Spatial and temporal variations of maize and wheat yield gaps and their relationships with climate in China

With the growth in population over the past decades, China’s food demand has been increasing. However, crops are facing greater water stress with the rising temperature and increasing drought in future. Therefore, identifying potential crop yield and its driving factors is necessary to ensure continued food production. In the present study, we estimated the potential yields of maize and wheat in China, with and without water limits, from 1985 to 2015 using a simple generic crop model (SIMPLE). We then investigated how climate factors (temperature, precipitation, and solar radiation) drove the spatial and temporal variation of yield gaps (quantified as the difference between potential yields with and without water limits). We found an increasing trend in the water limited yield gap of maize in northern China (3.9 ± 4.3 kg ha-1 yr-2) and wheat in central China (2.6 ± 2.4 kg ha-1 yr-2). The temporal trends of maize and wheat yield gaps in China were mainly related to precipitation and temperature. However, the spatial variation in maize yield gap trends was mainly related to the variations in precipitation, whereas for wheat it was attributed to changes in temperature or solar radiation. Upgrading irrigation infrastructure and management techniques or spatially adjusting crop types could help closing the yield gap of wheat in some zones.

Winter Wheat Yield Gaps Across the Loess Plateau of China

Winter Wheat Yield Gaps Across the Loess Plateau of China

Hydrothermal conditions impact yield, yield gap and water use efficiency of dryland wheat under different mulching practice in the Loess Plateau

Mulching practice reduces evaporation, modifies soil temperature and thereby affects crop yields. But the effects of mulching practices on moisture preservation and yield increase are not consistent under different precipitation and temperature conditions. The Loess Plateau spans a wide range, with distinct spatiotemporal variation in its hydrothermal conditions, so the reported effects of mulching are sometimes contradictory. A meta-analysis was carried out to evaluate the impact of plastic film mulching (PFM) and straw mulching (SM) on grain yields and water use efficiency (WUE) of wheat based on data from 74 peer-reviewed publications. The results showed that PFM and SM were significantly increased wheat yield and WUE compared with no-mulching (CK). The impact of mulching on wheat yields was also affected by temperature and precipitation. PFM led to a large yield and WUE increase under low-temperature conditions, the yield and WUE under SM was significantly affected by temperature and precipitation. Compared with PFM, SM showed no yield or WUE increasement effect under high temperature with low precipitation (> 10 °C, ≤ 350 mm) or low temperature with high precipitation (≤ 10 °C, > 350 mm). Meanwhile, our study found that PFM may reduce the yield gap between potential and actual yields compared to SM. These results have important implications for choosing proper mulching practices to increase wheat yields and WUE in agricultural production under local hydrothermal conditions.

Explaining Rice and Wheat Yield Gaps in Eastern Indian States: Insights from Stochastic Frontier Analysis

Explaining Rice and Wheat Yield Gaps in Eastern Indian States: Insights from Stochastic Frontier Analysis

A comprehensive method to increase yield and narrow the yield gap of winter wheat for sustainable intensification.

The agricultural production system is facing increasing demand pressure and environmental pressure. Green and efficient production methods are urgently needed in order to further enhance the yield of winter wheat and reduce the negative impact on the environment. Here, we analyzed the potential yield and yield gap of winter wheat in Shandong Province of China from 1981 to 2009. Meanwhile, we specified the effects of sowing time, irrigation and fertilization scheme, and variety characteristics on winter wheat. In the past 29 years, the yield gap in most areas of Shandong has become smaller, because the actual yield has increased and the potential yield has changed little under the background of climate change. In addition, it is found that delaying sowing date is beneficial to increase yield by helping winter wheat avoid adverse climate conditions. Also, an irrigation amount of 240 mm and nitrogen application amount of 180-210 kg ha-1 are best to maintain high yield, high resource utilization rate and low environmental pollution in this area. These suggested levels are lower than those currently used by many local farmers. Wheat varieties with longer grain-filling period and photoperiod response, higher grain-filling rate and grain weight were more adaptable to climate change. Improving agronomic management measures can significantly increase the yield of winter wheat and narrow the yield gap. This study can provide valuable information for improving the production potential of winter wheat, and for reducing the damage of agricultural activities to the environment. © 2022 Society of Chemical Industry.

Nitrogen nutrition index at GS 3.3 is an effective tool to adjust nitrogen required to reach attainable wheat yield

Current nitrogen (N) fertilization schedule for spring wheat was developed under a dominant crop-pasture rotation. After the year 2002, this cropping system was converted to continuous annual cropping systems under no-till, reducing soil N supply capacity progressively. Additionally, highest grain yield of new varieties increased N demand. The required additional N fertilizer can be adjusted by monitoring nutritional status of the crop. Our objectives were: i) to determine optimal N status at different phenological stages; ii) to quantify the wheat yield gap explained by N supply deficit, and iii) to assess the critical nitrogen nutrition index (NNI) value as a predictor of response to N fertilizer applied at GS 3.3. We adjusted the nitrogen dilution curve (Nc=4.17DM-0.31), deriving a critical NNI at GS 3.3 (NNI=1.24). Depending on soil N supply capacity and NNI at GS 3.3, wheat yield gap attributed to N supply deficit varied from 0 to 2.74 Mg ha-1, averaging 0.76 Mg ha-1. The critical NNI proposed at GS 3.3 was effective to diagnose the N crop demand to reach the attainable yield under different scenarios.

Environment and Nitrogen Rate Play Significant Roles in Winter Wheat Response to Nitrogen Management Intensification

Poor nitrogen (N) management is among the leading causes of winter wheat (Triticum aestivum L.) yield gaps in Kansas, and sowing date—which is impacted by crop rotation—is among the most important variables determining winter wheat’s attainable yields in the U.S. central Great Plains. This research aimed to investigate the relationship between N management strategies and various cropping systems in Kansas. The treatments consisted of nine combinations of three N management practices (standard, progressive, and green N) and five crop sequences (WtWt = continuous winter wheat; SyWt = winter wheat after soybean; TrSyWt = triticale (hay) – soybean – winter wheat rotation; CpWt = winter wheat after cowpea; TpDPwt = dual-purpose winter wheat after tepary bean; MoDPwt = dual-purpose winter wheat after moth bean). Standard N-management consisted of one single broadcast N application at 80 lb/a as UAN at spring greenup. Progressive N-management consisted of a split-N application at 40 and 27 lb/a each at greenup and jointing, using streamer bar nozzles and N-inhibitors added to the fertilizer. Green N management consisted of no fertilizer application except for the carryover N from the previous terminated legume crop. Crop sequences that allowed winter wheat to be sown at the optimum sowing date had the greatest yields. Green N management decreased dual-purpose winter wheat grain yield and shoot biomass. Both standard and progressive N management practices had similar results within crop sequences. Overall, our results suggested that intensive N management produced the same yields as the standard, but at lower N rates. Dual-purpose winter wheat combined with green N (i.e., relying exclusively on carryover N) was detrimental to winter wheat yield.

Increasing Winter Wheat Grain Yield by Replicating the Management Adopted in High-Yielding Commercial Fields

Large winter wheat yield gaps between farmer yields and yield potential in the southern Great Plains indicate the need to improve recommendations of best management strategies to profitably bridge this gap. Many studies have been completed on individual management factors pre-determined by the individual researcher, but we are not aware of studies comparing combination of practices that producers are currently using, which would be more relevant for real-world scenarios. Our objective was to determine the yield gains resulting from management intensification using combination of practices currently adopted in commercial wheat fields. Four management intensities (i.e., Low, Average, High, and Top) were derived from a survey of 656 commercial fields, and replicated in trials conducted in four and six locations in western and central Kansas. Management intensities were tested factorially on two adapted varieties. Grain yield in central Kansas ranged from 45.5 bu/a in the Low management intensity to 69.3 bu/a in the High and Top intensities, with the Average management increasing yields by 30% as compared to the Low intensity, and the High management increasing yields 18% from the Average. The variety WB4269 outyielded Zenda (63.2 and 58.7 bu/a) across central environments. In western Kansas, there was a significant variety by management interaction, where wheat yield increased from the Low and Average intensities to the High and Top intensities (72.8–78.9 to 90.7–96.0 bu/a). The WB-Grainfield and KS Dallas varieties produced similar yields in the western environments. Using similar management practices as the producers with high-yield results in central and western Kansas narrowed the yield gap, and further increases in management intensification were not warranted. Variety selection played an important role either by increasing attained yields or by interacting with management practices.

Improving wheat productivity reveals an emerging yield gap associated with short-term change in atmospheric humidity

Few studies have evaluated the effect of humid weather on wheat productivity and yield gaps. Humid weather conditions before, during, and after flowering cause losses associated with Fusarium head blight and pre-harvest sprouting. To increase the actual yield, efforts such as ensuring that farm yield is commensurate with yield potential via wheat breeding activities have been made in the wake of increasingly adverse climatic conditions. In the present study, the time-series wheat yield gap (simulated yield potential minus actual on-farm yield) was analyzed for the 1984–2020 period in the humid region of northern Japan, where major varieties have been replaced with more productive (high yield, preharvest resistant, and Fusarium head blight-resistant) ones almost every decade. The periodical introduction of alternative varieties has altered the meteorological factors responsible for yield gaps. Older varieties, ‘Chihoku-komugi’ and ‘Hokushin,’ exhibited higher sensitivity to atmospheric humidity before harvest and after the heading period. A new high-yield variety, ‘Kitahonami,’ with increased floret numbers, reduced the periodical mean yield gap from 1.95 Mg/ha to 1.14 Mg/ha, whereas the yield gap often expanded under vapor pressure deficit of < 0.3 kPa during the flowering period. The results indicated that ‘Kitahonami’ yield potentially depends on the yield rate of potential fertile florets, which leads to its vulnerability to short-term rainy, humid weather during the flowering period. Current breeding strategies reduce humidity damage at sensitive duration of preharvest sprouting-resistant, and Fusarium head blight-resistant, while the emerging yield gap factors continue to reflect the traits that could be targeted for improvement.