Highest Yield Gap Research Articles

Evaluation of forage maize yield gap using an integrated crop simulation model-satellite imagery method (Case study: four watershed basins in Golestan Province)

ABSTRACTThe yield gap provides a guide for prioritization of crop management options (such as optimized sowing date, seeding rate, irrigation schedule, soil fertility, fertilizer application, weed and pest control) in a studied area. This study aimed to determine the maize yield gap in four major watersheds in Golestan Province, Iran, using an integrated crop simulation model-satellite imagery method. The actual yield estimated by the NDVI (as the selected index) was between 8.89 and 20.40 t ha−1, while the potential yield is between 19.03 and 22.35 t ha−1. About 91.76% of the studied area had a yield less than 85% of the potential yield. The lowest actual yield was in the south, southeast, and north of the study area. The yield gap was estimated between 0 to 11.76 t ha−1 and 66.66% of maize farms yield gap was between 3.5 to 5.5 t ha−1. The yield gap fraction changed between 0 and 0.57. The results showed that soil-dependent variables, slope, and fluctuation in farm management factors (plant density, planting, irrigation, and various methods of weed control) caused the yield gap. High yield gap indicates that there is an opportunity to increase production through managerial optimization or excluding maize from cropping patterns.

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

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

Quantifying the spatial variation in the potential productivity and yield gap of winter wheat in China

Despite the improvement in cultivar characters and management practices, large gaps between the attainable and potential yields still exist in winter wheat of China. Quantifying the crop potential yield is essential for estimating the food production capacity and improving agricultural policies to ensure food security. Gradually descending models and geographic information system (GIS) technology were employed to characterize the spatial variability of potential yields and yield gaps in winter wheat across the main production region of China. The results showed that during 2000–2010, the average potential yield limited by thermal resource (YGT) was 23.2 Mg ha−1, with larger value in the northern area relative to the southern area. The potential yield limited by the water supply (YGW) generally decreased from north to south, with an average value of 1.9 Mg ha−1 across the entire study region. The highest YGW in the north sub-region (NS) implied that the irrigation and drainage conditions in this sub-region must be improved. The averaged yield loss of winter wheat from nutrient deficiency (YGN) varied between 2.1 and 3.1 Mg ha−1 in the study area, which was greater than the yield loss caused by water limitation. The potential decrease in yield from photo-thermal-water-nutrient-limited production to actual yield (YGO) was over 6.0 Mg ha−1, ranging from 4.9 to 8.3 Mg ha−1 across the entire study region, and it was more obvious in the southern area than in the northern area. These findings suggest that across the main winter wheat production region, the highest yield gap was induced by thermal resources, followed by other factors, such as the level of farming technology, social policy and economic feasibility. Furthermore, there are opportunities to narrow the yield gaps by making full use of climatic resources and developing a reasonable production plan for winter wheat crops. Thus, meeting the challenges of food security and sustainability in the coming decades is possible but will require considerable changes in water and nutrient management and socio-economic policies.

Profitability and yield gap between research station and farm level of Binamoog-8

Estimating profitability and yield gap between research station and farm level of Binamoog-8 was conducted in five major Binamoog-8 growing areas of Bangladesh, namely Ishwardi, Magura, Natore, Jessore and Meherpur. The average cost of production in farm level of Binamoog-8 is Tk. 47373.55ha-1 and higher in Ishwardi (Tk. 53454.92 ha-1) followed by Meherpur, Magura, Natore and Jessore of Tk. 45160.28, Tk. 44300.14, Tk. 45232.95 and Tk. 48719.47 per hectare, respectively .The major shares of total cost were human labour, power tiller and irrigation. The net returns were Tk. 41160, Tk. 42902.30, Tk. 53603.18, Tk. 47494.60 and Tk. 40437.16 per hectare followed in Ishwardi, Meherpur, Magura, Natore and Jessore, respectively. The highest net return (Tk. 53603.18 ha-1) comes from Magura district for Binamoog-8.The undiscounted average benefit cost ratio over full cost basis were 1.77, 1.95, 2.21, 2.05 and 1.83 for Binamoog-8 in field level for Ishwardi, Meherpur, Magura, Natore and Jessore, respectively. The average yield gap of Binamoog-8 in farm level and research station was 129.05 kg per hectare. The yield gap between research station and farm level were 102.97 kg, 185.50 kg, 45.85 kg, 128.50 kg and 182.45 kg per hectare for Binamoog-8 in Ishwardi, Meherpur, Magura, Natore and Jessore, respectively. The highest yield gap between research station and field level was in Meherpur (185.50 kg per hectare) and lowest in Magura (45.85 kg per hectare). Farmers were facing various constraints in Binamoog-8 cultivation. Different strategies, such as lack of quality seed and fertilizer in appropriate time, lack of credit facilities, rainfall during germination period, insect infestation and pathogen infection, pod maturity in different times have been discussed as strategies to minimize yield gaps.Asian J. Med. Biol. Res. December 2016, 2(4): 685-688

Spatial variability of grape yield and its association with soil water depletion within a vineyard of arid northwest China

Spatially variable soil properties, combined with the complex interrelationship between crop yield with environmental factors often lead to spatio-temporal variability of crop yield across the field. The objectives of this study were to investigate spatial variability of grape yield and estimate the potential yield at various soil water depletion levels within a vineyard of arid northwest China. Grape yield and soil water contents at different times were measured at 135 georeferenced points in 2012, and 147 points in 2013 within a 7.6-ha vineyard. Geostatistical approach was used to describe the spatial variation in grape yield, while boundary line analysis was used to estimate the potential yield at various soil water depletion levels. Some selected soil properties between the low and high yield gap groups, defined as the difference between the actual yield and the estimated potential yield, were compared. The spatial structure of yield distribution in the field was similar between the two years. The upper boundary line between grape yield with soil water depletion was best fitted by a quadratic function for both years. When soil water depletion was relatively high, soil sand content and saturated hydraulic conductivity of 0–20cm soil were found to be significantly different between the high and low yield gap groups, while none of soil properties in the top 40cm soil were found to be important in determining the yield gap when soil water supply was limited. Boundary line analysis method could be a valuable tool in developing better management practices to improve overall yield according to available soil water conditions in the field.

Mapping Chinese Rice Suitability to Climate Change

<p>Climate change has the potential to affect Chinese rice production; however, the rice crop could become more suitable to new climatic conditions because of benefits derived from new agricultural technologies. In this paper, a county-level dataset and crop model were used to analyze actual rice yield suitability by measuring the yield gap and yield stability from 1980 to 2011 in 1561 counties of China. The results showed that the national yield gap between the actual and potential yields was approximately 23.0%, which is close to the threshold for profitable planting. However, a number of counties in the northeastern and southwestern regions showed a 30 to 50% yield gap, which indicates a relatively lower suitability of the rice. The rice yield stability results indicated that the actual stability has exceeded the potential stability in most of the counties of China, thus indicating a high level of suitability. Temporally, a decreasing trend was observed for both the yield gap and stability, suggesting that the suitability of rice in China has improved, which might be associated with the development of agricultural technology. The only noteworthy locations presenting a high yield gap and yield instability were several counties in the northeastern region. Since the northeastern region accounts for a significant proportion of China's rice production, further investigations should be conducted to identify the underlying causes of the yield gaps and determine methods of increasing the yield stability. The implementation of more suitable agricultural technology in the area is also suggested to improve the rice suitability in the region.</p>

Rain Fed Rice Agriculture under Climate Variability in Southeast Asia: The Case of Thailand

This study was undertaken to analyze historical climate variability and quantify its impacts on rice production in the Ping River Basin, Northern Thailand. Catchment based climate gradient trend showed Upper Ping received the highest rainfall followed by Lower Ping and Middle Ping, respectively. Temperature and evaporation increased with decreasing altitude. The Ping climate exhibited high spatiotemporal variability. Temperature had an increasing trend but no significant trend in rainfall during the period of 1961-2010. Multiple regression models significantly explained 54% (P<0.01) rice yield, 43% (P<0.01) total production, and 47% (P=0.01) rice area temporal variation by climatic factors variability in Ping Basin, respectively. A critical impact assessment of climate variability on rice production showed that highest yield gap of 48%, planted area loss as high as of 55% and yield loss of 32% were observed. These losses in planted area and productivity are of paramount importance from food security perspective opting appropriate adaptation strategy at basin level. Planting of rice could be done Mid May to end of May in Upper and Middle Ping catchments while end of May to first week of June in Lower Ping without waiting until Mid-June and early July. Our findings indicate that there is a need to modify rice crop calendar that would ensure farmers to obtain better yield. This proposed adjustment of crop calendar will encourage farmers to plant early in May and harvest in September to avoid risk of flooding in October as an adaptation strategy in the face of prevailing climate variability.

Yield gap analysis in major wheat growing areas of Khorasan province, Iran, through crop modelling

To increase wheat productivity from current levels, the yield gap, the difference between actual and potential yield, should be analyzed. A crop model was calibrated for three winter wheat cultivars and validated for a major wheat growing area to evaluate yield gaps in Khorasan province in northeastern Iran. The validated model was used to simulate long-term yield for 14 locations under three management conditions (potential, water-limited, nitrogen-limited). The results showed that the average simulated potential yield of 7.8Mgha−1 has not yet been realized by farmers and there is a large gap between the actual and potential production levels (about 5.2Mgha−1). The simulation showed considerable difference between cultivars for yield gap. When averaged over locations and seasons, the total yield gap obtained for Chamran wheat was 4.4Mgha−1, for Sionz was 6Mgha−1, and for Gascozhen was 6.1Mgha−1. Across locations, the proportion of yield gap from water-limitation was 1.7% to 19.5% and for nitrogen- limitation was 40% to 47%. The gap caused by other limiting and reducing factors was 33–57%. The highest yield gap was observed in Ghaen for Gascozhen (7.6Mgha−1) and the lowest was observed in Sarakhs for Chamran (3.1Mgha−1). The results of the study suggest that the average farm yield of about 2.6Mgha−1 in irrigated fields in Khorasan province is not limited by low genetic yield potential. This indicates that farmers should emphasize more on management factors such as water availability, timing of nitrogen application, and selection of cultivars adapted for each environment to reduce yield gap.

Economic and Institutional Determinants of Foreign Land Acquisition in Africa: An Empirical Analysis

Over the past decade, Africa has experienced unprecedented demand for large scale agricultural land. Africa accounts for more than half of the volume of land acquired and close to two-thirds of the total deals globally. Yet, there is dearth of empirical evidence on demand for arable land in Africa. This paper provides some econometric analysis of the key determinants of large scale land acquisition in Africa. Evidences from bivariate and multi-variate analyses show that both economic and institutional factors play some critical role in explaining demand for large scale arable land in Africa. Key determinants include high yield gap, low per capita income, abundant inland water resources, low property rights and prevalence of corruption. To make the recent surge in land demand beneficial in terms of agricultural transformation, improved livelihoods for the rural people and reduce poverty, it is important to ensure both economic and institutional factors create positive incentives to the local economy, rural farmers and the poor. This includes making fertilizer available, increasing local farmers' access to agricultural infrastructures (e.g. irrigation, tractors and storage facilities), investing in land registration and certification, and improving land governance and procedures.

The roles of livestock in developing countries

Livestock play a significant role in rural livelihoods and the economies of developing countries. They are providers of income and employment for producers and others working in, sometimes complex, value chains. They are a crucial asset and safety net for the poor, especially for women and pastoralist groups, and they provide an important source of nourishment for billions of rural and urban households. These socio-economic roles and others are increasing in importance as the sector grows because of increasing human populations, incomes and urbanisation rates. To provide these benefits, the sector uses a significant amount of land, water, biomass and other resources and emits a considerable quantity of greenhouse gases. There is concern on how to manage the sector's growth, so that these benefits can be attained at a lower environmental cost. Livestock and environment interactions in developing countries can be both positive and negative. On the one hand, manures from ruminant systems can be a valuable source of nutrients for smallholder crops, whereas in more industrial systems, or where there are large concentrations of animals, they can pollute water sources. On the other hand, ruminant systems in developing countries can be considered relatively resource-use inefficient. Because of the high yield gaps in most of these production systems, increasing the efficiency of the livestock sector through sustainable intensification practices presents a real opportunity where research and development can contribute to provide more sustainable solutions. In order to achieve this, it is necessary that production systems become market-orientated, better regulated in cases, and socially acceptable so that the right mix of incentives exists for the systems to intensify. Managing the required intensification and the shifts to new value chains is also essential to avoid a potential increase in zoonotic, food-borne and other diseases. New diversification options and improved safety nets will also be essential when intensification is not the primary avenue for developing the livestock sector. These processes will need to be supported by agile and effective public and private institutions.

Radon diffusion through thin plastics

Soil nutrient depletion is one of the major causes of high yield gaps and nutrient deficiencies in East Africa highlands, including Rwanda. This research sought to determine the current soil nutrient balance and its spatial variation in 10 Rwandan agro-ecological zones. Soil nitrogen (N), phosphorus (P) and potassium (K) depletion in croplands were calculated using data from 455 field trials of the Optimizing Fertilizer Recommendations in Africa (OFRA) project in Rwanda. Calculated soil nutrient balances (NPK) and 15 environmental covariates were used to calibrate soil nutrient depletion models using ensemble machine learning (EML) and 10-fold cross-validation. In the 2019–2020 growing season, annual N and K depletions were 33.6 kg N ha−1 yr−1 and 71.0 kg K ha−1 yr−1, with a positive P balance of 2.30 kg P ha−1 yr−1. High soil nutrient uptake and high soil nutrient loss due to erosion and leaching were two main causes of NPK depletion. Spatial variations of NPK balance were influenced by soil nutrient stocks, soil erosion, elevation, rainfall, soil texture, and soil bulk density. The 10-fold cross-validation showed that coefficients of determination (R2) of NPK models were 62%, 58%, and 58%, respectively. Compared to single models, ensemble machine learning improved NPK model accuracy up to 5%. Our research revealed that soil nutrient depletion was highest in the northwest and lowest in the southeast of the study area. We conclude that increasing soil nutrient inputs without reducing soil nutrient loss due to soil degradation will not decrease soil nutrient depletion in Rwanda and ensemble machine learning outperforms single models in predicting soil nutrient balance. The solution to reduce high soil nutrient depletion in all agro-ecological zones of Rwanda would be to prioritize soil and water conservation measures and increase soil nutrient inputs.