Abstract

The soil profile and its spatial distribution are two essential aspects for promoting sustainable agriculture, with precise inputs in quantity, space, and time. This work’s objective was to elaborate a digital map of soil fertility considering the complete profile for the accurate management of amendments and fertilizers. For the preparation of the soil fertility map, the following inputs were used: a digital elevation model, information from 44 soil profiles, the conversion of the properties of the soil profiles into surface units, geostatistical analysis of the soil properties, and the preparation of the final map with a geographic information system. The best spatial models were achieved with CEC, pH, Ca, Mg, Na, and K. The map of the soil fertility classes was produced considering CEC and the pH value. The soil fertility classes presented the following sequence of occupied surfaces: very low, medium, very high, high, and low. A process was generated to elaborate digital maps (geostatistics) of soil fertility using taxonomic information from soil profiles and considering the complete profile. The process converts soil classification into geographic and soil fertility information from basic science to application.

Highlights

  • Sustainable development objectives require a type of agriculture oriented toward caring for the environment with the highest possible yields [1,2]

  • It is necessary to leave behind concepts such as the topsoil without taking into account the complete soil profile; the use of regional fertilization rates based on NPK, without taking into account the diversity of soils; and the application of amendments and other agricultural inputs without a precise diagnosis

  • The K map should be used with caution because the parameters of the spatial model are of the lowest quality due to the high random variability indicated by the value of the nugget

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Summary

Introduction

Sustainable development objectives require a type of agriculture oriented toward caring for the environment with the highest possible yields [1,2]. Precision agriculture is not being carried out in developing countries due to several reasons, such as the excessive division of land into small plots, the lack of capacity in data generation to produce plot maps, the insufficient technical ability of the producers to make the diagnoses, and the scarcity of laboratories, among others [2,11]. Given this situation, the generation of methodological strategies is required to achieve better diagnoses of soil fertility

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