Soil and Climate Characterization to Define Environments for Summer Crops in Senegal
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 characterizat...
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creator | Hernández, Carlos Manuel Faye, Aliou Ly, Mamadou Ousseynou Stewart, Zachary P Vara Prasad, P. V Bastos, Leonardo Mendes Nieto, Luciana Carcedo, Ana J. P Ciampitti, Ignacio Antonio |
description | 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. |
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V ; Bastos, Leonardo Mendes ; Nieto, Luciana ; Carcedo, Ana J. P ; Ciampitti, Ignacio Antonio</creator><creatorcontrib>Hernández, Carlos Manuel ; Faye, Aliou ; Ly, Mamadou Ousseynou ; Stewart, Zachary P ; Vara Prasad, P. V ; Bastos, Leonardo Mendes ; Nieto, Luciana ; Carcedo, Ana J. P ; Ciampitti, Ignacio Antonio</creatorcontrib><description>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.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su132111739</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Agricultural ecosystems ; Agricultural production ; Agriculture ; Cation exchange ; Cation exchanging ; Classification ; Climate ; Climate variability ; Climatic data ; Crop yield ; Crops ; Crops and soils ; Datasets ; Electrical conductivity ; Electrical resistivity ; Environmental aspects ; Evapotranspiration ; Fertilizers ; Food security ; Land use ; Land use management ; Livestock ; Nitrogen ; Phosphorus ; Potassium ; Precipitation ; Principal components analysis ; Productivity ; Seasons ; Soil fertility ; Soil investigations ; Soils ; Sorghum ; Summer ; Sustainability ; Temperature ; Topography ; Variability</subject><ispartof>Sustainability, 2021-11, Vol.13 (21), p.11739</ispartof><rights>COPYRIGHT 2021 MDPI AG</rights><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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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. 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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. 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subjects | Agricultural ecosystems Agricultural production Agriculture Cation exchange Cation exchanging Classification Climate Climate variability Climatic data Crop yield Crops Crops and soils Datasets Electrical conductivity Electrical resistivity Environmental aspects Evapotranspiration Fertilizers Food security Land use Land use management Livestock Nitrogen Phosphorus Potassium Precipitation Principal components analysis Productivity Seasons Soil fertility Soil investigations Soils Sorghum Summer Sustainability Temperature Topography Variability |
title | Soil and Climate Characterization to Define Environments for Summer Crops in Senegal |
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