Soil and Climate Characterization to Define Environments for Summer Crops in Senegal

Carlos Manuel Hernández, Aliou Faye, Mamadou Ousseynou Ly, Zachary P. Stewart, P. V. Vara Prasad, Leonardo Mendes Bastos, Luciana Nieto, Ana J. P. Carcedo and Ignacio Antonio Ciampitti
Additional contact information
Carlos Manuel Hernández: Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
Aliou Faye: ISRA Regional Centre of Excellence on Dry Cereals and Associated Crops, Thies 3320, Senegal
Mamadou Ousseynou Ly: National Centre for Livestock Research (CRZ), Kolda 2312, Senegal
Zachary P. Stewart: USAID, Bureau for Resilience and Food Security, Washington, DC 20523, USA
P. V. Vara Prasad: Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
Leonardo Mendes Bastos: Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
Luciana Nieto: Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
Ana J. P. Carcedo: Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
Ignacio Antonio Ciampitti: Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA

Sustainability, 2021, vol. 13, issue 21, 1-17

Abstract: Investigating soil and climate variability is critical to defining environments for field crops, understanding yield-limiting factors, and contributing to the sustainability and resilience of agro-ecosystems. Following this rationale, the aim of this study was to develop a soil–climate characterization to describe environmental constraints in the Senegal summer-crops region. For the soil database, 825 soil samples were collected characterizing pH, electrical conductivity (EC), phosphorus (P), potassium (K), cation exchange capacity (CEC), and total carbon (C) and nitrogen (N). For the climate, monthly temperature, precipitation, and evapotranspiration layers were retrieved from WorldClim 2.1, CHIRPS and TERRACLIMATE. The same analysis was applied individually to both databases. Briefly, a principal component analysis (PCA) was executed to summarize the spatial variability. The outcomes from the PCA were subjected to a spatial fuzzy c-means algorithm, delineating five soil and three climate homogeneous areas, accounting for 73% of the soil and 88% of the climate variation. To our knowledge, no previous studies were done with large soil databases since availability field data is often limited. The use of soil and climate data allowed the characterization of different areas and their main drivers. The use of this classification will assist in developing strategic planning for future land use and capability classifications.

Keywords: geospatial analysis; environmental classification; spatial; temporal; variability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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