Can yield variability be explained? Integrated assessment of maize yield gaps across smallholders in Ghana

AbstractIn sub-Saharan Africa (SSA), precipitation is an important driver of agricultural production. In Uganda, maize production is essentially rain-fed. However, due to changes in climate, projected maize yield targets have not often been met as actual observed maize yields are often below simulated/projected yields. This outcome has often been attributed to parallel gaps in precipitation. This study aims at identifying maize yield and precipitation gaps in Uganda for the period 1998–2017. Time series historical actual observed maize yield data (hg/ha/year) for the period 1998–2017 were collected from FAOSTAT. Actual observed maize growing season precipitation data were also collected from the climate portal of World Bank Group for the period 1998–2017. The simulated or projected maize yield data and the simulated or projected growing season precipitation data were simulated using a simple linear regression approach. The actual maize yield and actual growing season precipitation data were now compared with the simulated maize yield data and simulated growing season precipitation to establish the yield gaps. The results show that three key periods of maize yield gaps were observed (period one: 1998, period two: 2004–2007 and period three: 2015–2017) with parallel precipitation gaps. However, in the entire series (1998–2017), the years 2008–2009 had no yield gaps yet, precipitation gaps were observed. This implies that precipitation is not the only driver of maize yields in Uganda. In fact, this is supported by a low correlation between precipitation gaps and maize yield gaps of about 6.3%. For a better understanding of cropping systems in SSA, other potential drivers of maize yield gaps in Uganda such as soils, farm inputs, crop pests and diseases, high yielding varieties, literacy, and poverty levels should be considered.

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Soil and management‐related factors contributing to maize yield gaps in western Kenya

Food and Energy Security ◽

10.1002/fes3.189 ◽

2020 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Sussy Munialo ◽

A. Sigrun Dahlin ◽

Cecilia Onyango M. ◽

W. Oluoch‐Kosura ◽

Håkan Marstorp ◽

...

Keyword(s):

Maize Yield ◽

Related Factors ◽

Western Kenya ◽

Yield Gaps

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Identifying Ways to Narrow Maize Yield Gaps Based on Plant Density Experiments

Agronomy ◽

10.3390/agronomy10020281 ◽

2020 ◽

Vol 10 (2) ◽

pp. 281

Author(s):

Jian Li ◽

Man Wu ◽

Keru Wang ◽

Bo Ming ◽

Xiao Chang ◽

...

Keyword(s):

Management Practices ◽

Field Experiments ◽

Plant Density ◽

Population Level ◽

Maize Yield ◽

Limiting Factors ◽

Stable Range ◽

Maize Production ◽

Yield Gaps ◽

Loss Range

Exploring the maximum grain yields (GYs) and GY gaps in maize (Zea mays L.) can be beneficial for farmer to identify the GY-limiting factors and take adaptive management practices for a higher GY. The objective of this work was to identify the optimum maize plant density range and the ways to narrow maize GY gaps based on the variation of the GYs, dry matter (DM) accumulation and remobilization with changes in plant density. Field experiments were performed at the 71 Group and Qitai Farm in Xinjiang, China. Two modern cultivars, ZhengDan958 and ZhongDan909, were planted at 12 densities, ranging from 1.5 to 18 plants m−2. With increased plant density, single plant DM decreased exponentially, whereas population-level DM at the pre- (DMBS) and post- (DMAS) silking stages increased, and the amount of DM remobilization (ARDM) increased exponentially. Further analysis showed that plants were divided four density ranges: range I (<6.97 plants m−2), in which no DM remobilization occurred, DMBS and DMAS correlated significantly with GY; range II (6.97–9.54 plants m−2), in which the correlations of DMBS, DMAS, and ARDM with GY were significant; range III (9.54–10.67 plants m−2), in which GY and DMAS were not affected by density, DMBS increased significantly, and only the correlation of DMAS with GY was significant; and range IV (>10.67 plants m−2), in which the correlations of DMBS and ARDM with GY decreased significantly, while that of DMAS increased significantly. Therefore, ranges I and II were considered to be DM-dependent ranges, and a higher GY could be obtained by increasing the population-level DMAS, DMAS, and ARDM. Range III was considered the GY-stable range, increasing population-level DMBS, as well as preventing the loss of harvest index were the best way to enhance maize production. Range IV was interpreted as the GY-loss range, and a higher GY could be obtained by preventing the loss of HI and population-level DMAS.

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Unravelling the variability and causes of smallholder maize yield gaps in Ethiopia

Food Security ◽

10.1007/s12571-019-00981-4 ◽

2019 ◽

Vol 12 (1) ◽

pp. 83-103 ◽

Cited By ~ 4

Author(s):

Banchayehu Tessema Assefa ◽

Jordan Chamberlin ◽

Pytrik Reidsma ◽

João Vasco Silva ◽

Martin K. van Ittersum

Keyword(s):

Stochastic Frontier Analysis ◽

National Level ◽

Total Yield ◽

Maize Yield ◽

Sub Saharan Africa ◽

Yield Gap ◽

Potential Yield ◽

Frontier Analysis ◽

Yield Gaps ◽

Maize Yields

AbstractEthiopia has achieved the second highest maize yield in sub-Saharan Africa. Yet, farmers’ maize yields are still much lower than on-farm and on-station trial yields, and only ca. 20% of the estimated water-limited potential yield. This article provides a comprehensive national level analysis of the drivers of maize yields in Ethiopia, by decomposing yield gaps into efficiency, resource and technology components, and accounting for a broad set of detailed input and crop management choices. Stochastic frontier analysis was combined with concepts of production ecology to estimate and explain technically efficient yields, the efficiency yield gap and the resource yield gap. The technology yield gap was estimated based on water-limited potential yields from the Global Yield Gap Atlas. The relative magnitudes of the efficiency, resource and technology yield gaps differed across farming systems; they ranged from 15% (1.6 t/ha) to 21% (1.9 t/ha), 12% (1.3 t/ha) to 25% (2.3 t/ha) and 54% (4.8 t/ha) to 73% (7.8 t/ha), respectively. Factors that reduce the efficiency yield gap include: income from non-farm sources, value of productive assets, education and plot distance from home. The resource yield gap can be explained by sub-optimal input use, from a yield perspective. The technology yield gap comprised the largest share of the total yield gap, partly due to limited use of fertilizer and improved seeds. We conclude that targeted but integrated policy design and implementation is required to narrow the overall maize yield gap and improve food security.

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Using satellite remote sensing to understand maize yield gaps in the North China Plain

Field Crops Research ◽

10.1016/j.fcr.2015.07.004 ◽

2015 ◽

Vol 183 ◽

pp. 31-42 ◽

Cited By ~ 17

Author(s):

Yi Zhao ◽

XinPing Chen ◽

ZhenLing Cui ◽

David B. Lobell

Keyword(s):

Remote Sensing ◽

Satellite Remote Sensing ◽

North China Plain ◽

North China ◽

Maize Yield ◽

The North ◽

The North China Plain ◽

Yield Gaps

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Narrowing the Agronomic Yield Gaps of Maize by Improved Soil, Cultivar, and Agricultural Management Practices in Different Climate Zones of Northeast China

Earth Interactions ◽

10.1175/ei-d-15-0032.1 ◽

2016 ◽

Vol 20 (12) ◽

pp. 1-18 ◽

Cited By ~ 6

Author(s):

Zhijuan Liu ◽

Xiaoguang Yang ◽

Xiaomao Lin ◽

Kenneth G. Hubbard ◽

Shuo Lv ◽

...

Keyword(s):

Physical Properties ◽

Agricultural Production ◽

Northeast China ◽

Management Practices ◽

Growing Season ◽

Maize Yield ◽

Soil Physical Properties ◽

Potential Yield ◽

Climate Zones ◽

Yield Gaps

Abstract Northeast China (NEC) is one of the major agricultural production areas in China, producing about 30% of China’s total maize output. In the past five decades, maize yields in NEC increased rapidly. However, farmer yields still have potential to be increased. Therefore, it is important to quantify the impacts of agronomic factors, including soil physical properties, cultivar selections, and management practices on yield gaps of maize under the changing climate in NEC in order to provide reliable recommendations to narrow down the yield gaps. In this study, the Agricultural Production Systems Simulator (APSIM)-Maize model was used to separate the contributions of soil physical properties, cultivar selections, and management practices to maize yield gaps. The results indicate that approximately 5%, 12%, and 18% of potential yield loss of maize is attributable to soil physical properties, cultivar selection, and management practices. Simulation analyses showed that potential ascensions of yield of maize by improving soil physical properties PAYs, changing to cultivar with longer maturity PAYc, and improving management practices PAYm for the entire region were 0.6, 1.5, and 2.2 ton ha−1 or 9%, 23%, and 34% increases, respectively, in NEC. In addition, PAYc and PAYm varied considerably from location to location (0.4 to 2.2 and 0.9 to 4.5 ton ha−1 respectively), which may be associated with the spatial variation of growing season temperature and precipitation among climate zones in NEC. Therefore, changing to cultivars with longer growing season requirement and improving management practices are the top strategies for improving yield of maize in NEC, especially for the north and west areas.

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Statistically bias-corrected and downscaled climate models underestimate the severity of U.S. maize yield shocks

10.21203/rs.3.rs-157774/v1 ◽

2021 ◽

Author(s):

David Lafferty ◽

Ryan Sri ◽

Iman Haqiqi ◽

Thomas Hertel ◽

Klaus Keller ◽

...

Keyword(s):

Climate Models ◽

Maize Yield ◽

The United States ◽

Cmip5 Models ◽

Yield Variability ◽

Climate Information ◽

Model Ensemble ◽

Trade Offs ◽

Uncertainty Analyses ◽

Subsequent Incorporation

Abstract Efforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Previous studies typically focus their uncertainty analyses on climatic variables and are silent on how these uncertainties propagate into human systems through their subsequent incorporation into econometric models. Here, we use a multi-model ensemble of statistically downscaled and bias-corrected climate models, as well as the corresponding CMIP5 parent models, to analyze uncertainty surrounding annual maize yield variability in the United States. We find that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest historically observed yield shocks. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence.

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Spatio-Temporal Dynamics of Maize Potential Yield and Yield Gaps in Northeast China from 1990 to 2015

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16071211 ◽

2019 ◽

Vol 16 (7) ◽

pp. 1211 ◽

Cited By ~ 3

Author(s):

Luoman Pu ◽

Shuwen Zhang ◽

Jiuchun Yang ◽

Liping Chang ◽

Shuting Bai

Keyword(s):

Northeast China ◽

Temporal Dynamics ◽

Maize Yield ◽

Potential Yield ◽

Ecological Zones ◽

Yield Gaps ◽

Spatio Temporal ◽

Actual Yield ◽

Lower Temperature ◽

Center Area

Maize yield has undergone obvious spatial and temporal changes in recent decades in Northeast China. Understanding how maize potential yield has changed over the past few decades and how large the gaps between potential and actual maize yields are is essential for increasing maize yield to meet increased food demand in Northeast China. In this study, the spatial and temporal dynamics of maize potential yield in Northeast China from 1990 to 2015 were simulated using the Global Agro-ecological Zones (GAEZ) model at the pixel level firstly. Then, the yield gaps between actual and potential yields were analyzed at city scale. The results were the following. (1) The maize potential yield decreased by about 500 kg/ha and the potential production remained at around 260 million tonnes during 1990–2000. From 2000 to 2015, the maize potential yield and production increased by approximately 1000 kg/ha and 80 million tonnes, respectively. (2) The maize potential yield decreased in most regions of Northeast China in the first decade, such as the center area (CA), south area (SA), southwest area (SWA), and small regions in northeast area (NEA), due to lower temperature and insufficient rainfall. The maize potential yield increased elsewhere. (3) The maize potential yield increased by more than 1000 kg/ha in the center area (CA) in the latter 15 years, which may be because of the climate warming and sufficient precipitation. The maize potential yield decreased elsewhere and Harbin in the center area (CA). (4) In 40 cities of Northeast China, the rates of actual yield to potential yield in 17 cities were higher than 80%. The actual yields only attained 50–80% of the potential yields in 20 cities. The gaps between actual and potential yields in Hegang and Dandong were very large, which need to be shrunk urgently. The results highlight the importance of coping with climate change actively, arranging crop structure reasonably, improving farmland use efficiency and ensuring food security in Northeast China.

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