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

Loon, Marloes P. van; Adjei-Nsiah, S.; Descheemaeker, Katrien; Akotsen-Mensah, Clement; Dijk, Michiel van; Morley, Tom; Ittersum, M.K. van; Reidsma, P.

doi:10.1016/j.fcr.2019.03.022

Cited by 33 publications

(22 citation statements)

References 38 publications

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“…The study showed how an integrated approach in the analysis of maize yield gaps can provide complementary findings that are of wider relevance on smallholder farms. The study has particularly extended, as a new result, the recent findings by van Loon et al (2019), who used an integrated approach and identified only site-specific factors influencing maize yield gaps on smallholder farms. The high proportion of variance attributable to soil properties at both Mukuyu and Shikomoli, as demonstrated by FA (Tables 5 and 7), suggests that soil factors were more important in influencing maize yield gaps than management-related variables.…”

Section: Consistent Factors Influencing Maize Yield Gaps Regardlessmentioning

confidence: 89%

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

Munialo

Dahlin

Onyango

et al. 2019

Food and Energy Security

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The solution to reducing existing yield gaps on smallholder farms lies in understanding factors limiting yield in areas with agricultural intensification potential. This study applied an integrated analysis approach comprising Classification and Regression Tree (CART), generalized linear mixed model (GLMM), and factor analysis (FA), to explain soil and management‐related factors influencing maize yield gaps, in order to enhance yields. The study was conducted in Mukuyu and Shikomoli in western Kenya, sites with, respectively, high and low agroecological potential regarding soil fertility. Maize yield gaps were quantified by comparing yields on the 90th percentile of farms to yields determined in 189 fields on 70 randomly sampled smallholdings. Soil and management‐related factors were determined at early and late maize development stages. Maize yield on the 90th percentile of farms in Mukuyu and Shikomoli was 5.1 and 4.8 t/ha, respectively, and the average yield gap was 1.8 and 2.6 t/ha, representing 35% and 54% unachieved yield for Mukuyu and Shikomoli, respectively. In FA, soil was revealed to be the main factor influencing maize yield gaps at both sites, rather than management‐related variables. The CART method identified maize density, chlorophyll values, maize height, and depth to compact layer as consistent factors affecting yield at both sites, while GLMM identified soil texture (silt content) as important. According to CART, weed cover at early stages and maize density at late stages were the most limiting factor in maize production in Mukuyu and Shikomoli, respectively. Generalized linear mixed model analysis identified agroecology‐specific factors influencing maize yield gaps as soil‐available phosphorus and zinc, plus weed pressure at early maize stages in Mukuyu, and plus soil cation exchange capacity and exchangeable magnesium in Shikomoli. Through an integrated approach, it was possible to identify both consistent and agroecology‐specific factors limiting crop yields. This can increase the applicability of the findings to smallholder farms.

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Section: Consistent Factors Influencing Maize Yield Gaps Regardlessmentioning

confidence: 89%

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

Munialo

Dahlin

Onyango

et al. 2019

Food and Energy Security

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“…Cui et al (2013) reported that effective N management could narrow the maize yield gap while reducing greenhouse gas emissions. All studies agree that quantifying the potential yield and the yield gap for maize could help to reveal the factors that limit the yield, and ultimately lead to suggestions for technical management measures to narrow the existing yield gap (Wang et al, 2014;Liu et al, 2016;Agus et al, 2019;Marloes et al, 2019).…”

Section: Introductionmentioning

confidence: 85%

“…Raising the potential crop yield is difficult to achieve in a short time (Rincent et al, 2014;Li et al, 2017;Simkin et al, 2019), and therefore a substantial increase in current yields can only be achieved by narrowing the yield gap (Wang et al, 2014). Typically, the yield gap is defined as the difference between the average yield achieved by farmers in a given area over a certain period of time and the estimated reference yield (usually referred to as the potential yield or the water limit yield) (Maria Carolina et al, 2018;Agus et al, 2019;Marloes et al, 2019). The potential yield can be defined and measured in a number of ways, for example through using crop growth models, by conducting maximum yield trials, or by measuring the maximum yield achieved by farming households (Van Ittersum and Rabbinge, 1997;Lobell et al, 2009;Marloes et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Typically, the yield gap is defined as the difference between the average yield achieved by farmers in a given area over a certain period of time and the estimated reference yield (usually referred to as the potential yield or the water limit yield) (Maria Carolina et al, 2018;Agus et al, 2019;Marloes et al, 2019). The potential yield can be defined and measured in a number of ways, for example through using crop growth models, by conducting maximum yield trials, or by measuring the maximum yield achieved by farming households (Van Ittersum and Rabbinge, 1997;Lobell et al, 2009;Marloes et al, 2019). Currently, China's actual maize yield is only 58% of the average potential yield of 13,875 kg ha −1 , which makes for a yield gap of 42% (Global Yield Gap Atlas, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…This yield gap is the result of farmers not using suitable cultivars and production techniques. Narrowing the yield gap is a logical and modern strategy that has been touted as a solution to the world's need to increase global food production (Lobell et al, 2009;Liu et al, 2016;Marloes et al, 2019). Several approaches to narrowing the yield gap have been studied.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Narrowing Yield Gaps and Enhancing Nitrogen Utilization for Summer Maize (Zea mays L) by Combining the Effects of Varying Nitrogen Fertilizer Input and Planting Density in DSSAT Simulations

Ren

Cheng

et al. 2020

Front. Plant Sci.

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Within and among farm variability of coffee quality of smallholders in southwest Ethiopia

et al. 2023

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The biophysical drivers that affect coffee quality vary within and among farms. Quantifying their relative importance is crucial for making informed decisions concerning farm management, marketability and profit for coffee farmers. The present study was designed to quantify the relative importance of biophysical variables affecting coffee bean quality within and among coffee farms and to evaluate a near infrared spectroscopy-based model to predict coffee quality. Twelve coffee plants growing under low, intermediate and dense shade were studied in twelve coffee farms across an elevational gradient (1470-2325 m asl) in Ethiopia. We found large within farm variability, demonstrating that conditions varying at the coffee plant-level are of large importance for physical attributes and cupping scores of green coffee beans. Overall, elevation appeared to be the key biophysical variable influencing all the measured coffee bean quality attributes at the farm level while canopy cover appeared to be the most important biophysical variable driving the abovementioned coffee bean quality attributes at the coffee plant level. The biophysical variables driving coffee quality (total preliminary and specialty quality) were the same as those driving variations in the near-infrared spectroscopy data, which supports future use of this technology to assess green bean coffee quality. Most importantly, our findings show that random forest is computationally fast and robust to noise, besides having comparable prediction accuracy. Hence, it is a useful machine learning tool for regression studies and has potential for modeling linear and nonlinear

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

Cited by 33 publications

References 38 publications

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

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

Narrowing Yield Gaps and Enhancing Nitrogen Utilization for Summer Maize (Zea mays L) by Combining the Effects of Varying Nitrogen Fertilizer Input and Planting Density in DSSAT Simulations

Within and among farm variability of coffee quality of smallholders in southwest Ethiopia

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