The 2020 Maize Production Failure in Ghana: A Case Study of Ejura-Sekyedumase Municipality

Obour, Peter Bilson; Arthur, Isaac K.; Owusu, Kwadwo

doi:10.3390/su14063514

Cited by 12 publications

(3 citation statements)

References 20 publications

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“…Their study suggests that current maize models may not adequately capture the impact of climate extremes, specifically heat and drought, on maize photosynthesis and yield. In their survey, Obour et al [37] studied the primary causes of maize production failure in Ghana. They revealed that factors such as poor soil quality, inadequate farm inputs and under-resourced mechanisation had a lesser impact on maize production failure than drought conditions that are unfavourable for maize growth.…”

Section: Discussionmentioning

confidence: 99%

The Future Probability of Winter Wheat and Maize Yield Failure in Hungary Based on Long-Term Temporal Patterns

Huzsvai,

Juhász,

Seddik

et al. 2024

Sustainability

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The level of yield variation of primary crops has a considerable effect on the vulnerability of agriculture. The main factor that makes the agriculture of Hungary so vulnerable is climate change, and technological development cannot compensate for its unfavourable effects. We examined the yield failures of the two major field crops grown in Hungary that occurred during the last 100 years. The goals of our study were to determine how often yield losses at 15% and 30% occur, what their probability is and whether the probability has changed in recent decades. The Wald–Wolfowitz runs test was used to determine the randomness of yield failures. A series of yield failures for maize and winter wheat were found to be random. Based on the data for 1985–2023, failure by 15% and 30% can be expected approximately every 8th and 19th year for winter wheat and 3rd and 5th year for maize. Winter wheat yield failure at 15% shows a decreasing trend in occurrence, while at 30% it increases. On the other hand, the frequency of maize yield failure increased at both levels. The consideration of historical yield data can help to determine the extent of crop loss to be expected in the long term to maintain sustainable winter wheat and maize production in our changing climate.

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Section: Discussionmentioning

confidence: 99%

The Future Probability of Winter Wheat and Maize Yield Failure in Hungary Based on Long-Term Temporal Patterns

Huzsvai,

Juhász,

Seddik

et al. 2024

Sustainability

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“…In this sense, maize is one of the most important cereals in the world and a staple food in many households. It is also a source of animal feed and a fundamental product in the food industry [2,3]. World production is estimated at 1192 Mt, with the largest producers being the United States, China, Brazil and Mexico [4].…”

Section: Introductionmentioning

confidence: 99%

Yield Predictions of Four Hybrids of Maize (Zea mays) Using Multispectral Images Obtained from UAV in the Coast of Peru

et al. 2022

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Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability for the farmer’s economy. In this study, we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using vegetation indices (VIs). A total of 10 VIs (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. Highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA showed clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimating the performance, showing greater precision at 51 DAS. The use of unmanned aerial vehicles (UAVs) to monitor crops allows us to optimize resources and helps in making timely decisions in agriculture in Peru.

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“…In this sense, corn is one of the most important cereals in the world and a staple food in many households. It is also a source in animal feed and a fundamental product in the food industry [2,3]. World production is estimated at 1,192 Mt, with the largest producers being the United States, China, Brazil and Mexico [4].…”

Section: Introductionmentioning

confidence: 99%

Yield Predictions of Four Hybrids of Maize (Zea mays) using Multispectral Images Obtained from RPAS in the Coast of Peru

Saravia¹,

Salazar²,

Valqui-Valqui³

et al. 2022

Preprint

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Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability in the farmer's economy. In this study we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using remotely sensed spectral vegetation indices (VI). A total of 10 VI (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. In the present study, highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA indicated a clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimate the performance, showing greater precision at 51 DAS. The use of RPAS to monitor crops allows optimizing resources and helps in making timely decisions in agriculture in Peru.

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The 2020 Maize Production Failure in Ghana: A Case Study of Ejura-Sekyedumase Municipality

Cited by 12 publications

References 20 publications

The Future Probability of Winter Wheat and Maize Yield Failure in Hungary Based on Long-Term Temporal Patterns

The Future Probability of Winter Wheat and Maize Yield Failure in Hungary Based on Long-Term Temporal Patterns

Yield Predictions of Four Hybrids of Maize (Zea mays) Using Multispectral Images Obtained from UAV in the Coast of Peru

Yield Predictions of Four Hybrids of Maize (Zea mays) using Multispectral Images Obtained from RPAS in the Coast of Peru

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