Identifying drivers for variability in maize (Zea mays L.) yield in Ghana: A meta-regression approach

Kouame, Anselme K. K.; Bindraban, P.S.; Kissiedu, Isaac N.; Atakora, Williams K.; Mejahed, Khalil El

doi:10.1016/j.agsy.2023.103667

Cited by 6 publications

(5 citation statements)

References 77 publications

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“…The estimated yields in this study were lower than the yields reported by MoFA due to differences in estimation methods (Adzawla et al, 2023). Consistent with previous studies (Buah et al, 2017;Klutse et al, 2018;Adzawla et al, 2021a;Boullouz et al, 2022;Kouame et al, 2023), the use of different fertilizer formulations resulted in a significant increase in yield among the farmers. Nonetheless, the AE and PFP values estimated in this study were low at 2.0-2.9 and 13.7-18.5 kg/kg, respectively (Table 5).…”

Section: Fertilizer Use Efficiency Among Maize Farmerssupporting

confidence: 88%

“…Similar results were obtained earlier by Morris et al (2007) and Xu et al (2009), who found that low fertilizer use in Africa is largely due to its low profitability. Despite the established positive impact of fertilizer use on crop yields (Adzawla et al, 2021a,b;Kouame et al, 2023), some farmers are either demotivated or hindered by compelling reasons not to use the fertilizers (Adzawla et al, 2021b). The emerging low profitability coupled with the low FUE discussed in the previous section can be a dominant factor explaining the demotivation to use fertilizer by some farmers.…”

Section: Economic Incentives For Fertilizer Usementioning

confidence: 97%

“…These include the high cost of fertilizers, limited availability and accessibility of quality fertilizers, and a lack of knowledge and information among farmers about appropriate fertilizer use. Nevertheless, several studies have concluded that fertilizer use leads to an increase in maize yields in the country (Tahiru et al, 2015;Buah et al, 2017;Ragasa and Chapoto, 2017;Asante et al, 2019;Adzawla et al, 2021a,b;Kouame et al, 2023). For example, using data from a controlled experiment, Tahiru et al (2015) estimated yield increases from 84 to 90% due to fertilizer application.…”

Section: Introductionmentioning

confidence: 99%

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Fertilizer use efficiency and economic viability in maize production in the Savannah and transitional zones of Ghana

Adzawla,

Setsoafia,

Setsoafia

et al. 2024

Front. Sustain. Food Syst.

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Increasing fertilizer use is highly justified for sustainable agricultural intensification if yield response, fertilizer use efficiency (FUE), and economic viability of fertilizer application are high. Despite the increasing fertilizer application rates in Ghana, yields only marginally increased. Also, the recent fertilizer price hikes post COVID-19 revived concern for economic analysis of fertilizers. This study analyzed the FUE and economic viability of fertilizer use in maize production in Guinea/Sudan Savannah and Transitional/Deciduous zones of Ghana. Survey data from 2,673 farmers in the 2019, 2020, and 2021 production seasons were used. The average agronomic efficiency (AE), partial factor productivity (PFP), and value-cost ratio (VCR) of fertilizer use were 2.2 kg of grains per kilogram of fertilizer, 18.3 kg grains per kilogram of fertilizer, and 1.8 Ghana cedis of marginal yield per Ghana cedi spent on fertilizer, respectively. Fertilizer use was economically viable for only 28.1% of farmers with a VCR of 2 or higher, while 52.5% reached the break-even point with a VCR of at least 1. Various fertilizer formulations, including NPK plus sulfur, and adoption of integrated soil fertility management (ISFM) practices, particularly improved seeds, organic fertilizers, and minimum tillage, improved maize yield response to fertilizer and thus the FUE. These low efficiency and economic viability of fertilizer use are prevailing conditions in other sub-Saharan Africa (SSA) countries and these do not guarantee sustainable food security and improved livelihood of the farmers in the region. Ghana’s Ministry of Food and Agriculture (MoFA), together with relevant stakeholders, should provide guidance on ISFM and intensify farmer education through farmer associations to increase the adoption of ISFM. The local government should work with other relevant stakeholders to improve the market conditions within the agriculture sector, for instance, by linking farmers to city markets for favorable output prices.

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Section: Fertilizer Use Efficiency Among Maize Farmerssupporting

confidence: 88%

Section: Economic Incentives For Fertilizer Usementioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Fertilizer use efficiency and economic viability in maize production in the Savannah and transitional zones of Ghana

Adzawla,

Setsoafia,

Setsoafia

et al. 2024

Front. Sustain. Food Syst.

Self Cite

View full text Add to dashboard Cite

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“…Similarly, agricultural landscape characterization using agroclimatic zoning has been adopted to upscale crop yield by examining production heterogeneities within and between zones (van Wart et al, 2013). In analyzing these responsive differences, previous studies have established significant differences in agricultural productivity across various AEZs using statistical and process-based models (Asante et al, 2019;Kouame et al, 2023). The variability in production stems from differences in management practices, weather conditions, soil characteristics, and agricultural landscapes (Musafiri et al, 2022), and their influences vary across humid, semihumid, semiarid, and arid AEZs.…”

Section: Introductionmentioning

confidence: 99%

Agronomic management response in maize (Zea maysL.) production across three agroecological zones of Kenya

Kipkulei,

Bellingrath‐Kimura,

Lana

et al. 2024

Agrosystems Geosci & Env

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Maize (Zea mays L.) productivity in Kenya has witnessed a decline attributed to the effects of climate change and biophysical constraints. The assessment of agronomic practices across agroecological zones (AEZs) is limited by inadequate data quality, hindering a precise evaluation of maize yield on a large scale. In this study, we employed the DSSAT‐CERES‐Maize crop model (where CERES is Crop Environment Resource Synthesis and DSSAT is Decision Support System for Agrotechnology Transfer) to investigate the impacts of different agronomic practices on maize yield across different AEZs in two counties of Kenya. The model was calibrated and evaluated with observed grain yield, biomass, leaf area index, phenology, and soil water content from 2‐year experiments. Remote sensing (RS) images derived from the Sentinel‐2 satellite were integrated to delineate maize areas, and the resulting information was merged with DSSAT‐CERES‐Maize yield simulations. This facilitated a comprehensive quantification of various agronomic measures at pixel scales. Evaluation of agronomic measures revealed that sowing dates and cultivar types significantly influenced maize yield across the AEZs. Notably, AEZ II and AEZ III exhibited elevated yields when implementing combined practices of early sowing and cultivar H614. The impacts of optimal management practices varied across the AEZs, resulting in yield increases of 81, 115, and 202 kg ha−1 in AEZ I, AEZ II, and AEZ III, respectively. This study underscores the potential of the CERES‐Maize model and high‐resolution RS data in estimating production at larger scales. Furthermore, this integrated approach holds promise for supporting agricultural decision‐making and designing optimal strategies to enhance productivity while accounting for site‐specific conditions.

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“…The drivers of crop yield observable and exploitable by remote sensing applications include soil composition, nutrient availability, water quality and content, air and ground temperature (growing degree days [GDD]), plant density, plant height, sowing date, fraction of absorbed photosynthetically active radiation (fAPAR), leaf chlorophyll concentration/content (LCC), and carotenoid content (CC), among others [ 16 , 17 , 18 , 19 ]. Regression models for these yield drivers rely on the sensor’s sampling spectrum, applying this reflectance data to the calculation of vegetation indices (VIs) for correlation analysis.…”

Section: Introductionmentioning

confidence: 99%

Spatial and Spectral Dependencies of Maize Yield Estimation Using Remote Sensing

Burglewski,

Srinivasagan,

Ketterings

et al. 2024

Sensors

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Corn (Zea mays L.) is the most abundant food/feed crop, making accurate yield estimation a critical data point for monitoring global food production. Sensors with varying spatial/spectral configurations have been used to develop corn yield models from intra-field (0.1 m ground sample distance (GSD)) to regional scales (>250 m GSD). Understanding the spatial and spectral dependencies of these models is imperative to result interpretation, scaling, and deploying models. We leveraged high spatial resolution hyperspectral data collected with an unmanned aerial system mounted sensor (272 spectral bands from 0.4–1 μm at 0.063 m GSD) to estimate silage yield. We subjected our imagery to three band selection algorithms to quantitatively assess spectral reflectance features applicability to yield estimation. We then derived 11 spectral configurations, which were spatially resampled to multiple GSDs, and applied to a support vector regression (SVR) yield estimation model. Results indicate that accuracy degrades above 4 m GSD across all configurations, and a seven-band multispectral sensor which samples the red edge and multiple near-infrared bands resulted in higher accuracy in 90% of regression trials. These results bode well for our quest toward a definitive sensor definition for global corn yield modeling, with only temporal dependencies requiring additional investigation.

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Identifying drivers for variability in maize (Zea mays L.) yield in Ghana: A meta-regression approach

Cited by 6 publications

References 77 publications

Fertilizer use efficiency and economic viability in maize production in the Savannah and transitional zones of Ghana

Fertilizer use efficiency and economic viability in maize production in the Savannah and transitional zones of Ghana

Agronomic management response in maize (Zea maysL.) production across three agroecological zones of Kenya

Spatial and Spectral Dependencies of Maize Yield Estimation Using Remote Sensing

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