Assessing the impact of climate variability on maize yields in the different regions of Ghana—A machine learning perspective

different climatic conditions; (iv) to provide policymakers in Ghana with evidence-based rec- ommendations on how to address the challenges posed by climate change and ensure food security for the country’s growing population; and finally, (v) to add to the growing literature of the predictive power of stacking ensemble machine learning model to agricultural and cli- mate change studies. Paper structure This study is divided into five primary sections. Aside from the study’s background, the paper is arranged in the following manner. The second section outlines the empirical data, the data preprocessing techniques applied, the machine learning models suggested for maize yield pre- dictions, and the methods used for empirical evaluation. After that, the main results of the study are presented in the third section. We then present a discussion of key findings in section four. Finally, we conclude the study and present some recommendations for future studies in section five. Materials and methods Areas under study Table 1 shows the districts under each region in Ghana, and the agro-ecological zones are pre- sented in Table 2. Data collection Monthly data for the climate variables (average temperature, minimum temperature, maxi- mum temperature, precipitation, and CO2) were obtained from the Ghana Meteorological Agency for all the regions in Ghana from 1992 to 2019. The reason for incorporating average, minimum, and maximum temperatures as predictors in the same model stemmed from the Table 1. The regions in Ghana and major districts where there is maize production.

Region Major Districts Western Wassa Amanfi, Aowin-Suaman, Wassa East, Shama Ahanta East, Sefwi-Wiaso, West Ahanta, Mporhor, Wassa West, Bibiani-Anhwiaso-Bekwai, Juabeso-Bea, East Nzema, Jomoro Central Awutu-Efutu-Senya, Upper Denkyira, Twifo-Herman/L.Den, Komenda-Edina-Equafo, Assin-Fosu, Gomoa, Mfantsiman, Ajumako-Essiam-Enyana, Cape Coast, Agona, Asikuma-Odoben-Brakwa, Abura-Asebu-Kwamankese Volta Kpando, Adidome, Sogakope, Akatsi, Keta, Denu/Dzodze (Ketu), Hohoe, Jasikan, Nkwanta, Kadjebi, Keta-Krachi Northern Tonlon/Kumbugu, Gushiegu/Karaga, Damango, Zabzugu/Tatale, Yendi, Bimbila, Walewale, Gambaga, Tamale, Sevelugu/Nanton, Salaga, Bole, Saboba/Chereponi Ashanti Adansi-North, Amansie East, Amansie West, Ahafo Ano North, Asante Akim North, Ejura Sekyeredumase, Sekyere West, Kumasi Metropolitan Assembly, Sekyere East, Adansi West, Ahafo Ano South, Atwima, Ejisu Juaben, Bosumtwe Kwanwoma, Kwabre Sekyere, Offinso, Adansi East, Asante Akim South, Afigya Sekyere Eastern Kwahu North, Birim South, Fanteakwa, Birim North, West Akim, Akwapim North, Suhum/Kraboa/ Coalta, Akwapim South, New Juabeng, East Akim, Kwahu South, Manya Krobo, Yilo Krobo, Kwaebibirim/Kade, Asuogyaman Greater Accra Tema, Ga, West Dangbe, East Dangbe Brong Ahafo Dorma Ahenkro, Sunyani, Asutifi, Wenchi, Berekum, Tano, Kintampo, Sene, Nkoranza, Jaman, Atebubu, Techiman, Asunafo Upper West Wa, Lawra, Tumu, Jirapa, Nadowli Upper East Builsa, Kasina/Nankani, Bongo, Bolgatanga, Bawku East, Bawku West https://doi.org/10.1371/journal.pone.0305762.t001 PLOS ONE

Impact of climate variability on maize yields in Ghana PLOS ONE | https://doi.org/10.1371/journal.pone.0305762 June 25, 2024 5 / 24