Chapters authored
Use of Swine Manure in Agriculture in Southern Brazil: Fertility or Potential Contamination?
A major challenge in agricultural production systems is the maximization of resources used to promote the development of crops with a minimum of environmental impact. In this sense, the use of fertilizers of animal origin has great potential to promote the improvement of soil properties. In southern Brazil, swine manure (SM) is widely used in agricultural areas, allowing nutrient cycling within pig units and reducing costs for chemical fertilizers. Much of this manure is applied in liquid form (PS), but other strategies are often used, such as PS compost and swine bedding (DL). The use of these SMs improves the chemical, biological, and physical attributes of the soil, contributing to increased fertility and productivity of crops. However, prolonged use or applications with high doses of SM can result in the accumulation of metals and phosphorus in soils, representing a risk of contamination of soils and surface water resources, mainly due to losses by runoff, and subsurface, by leaching. Therefore, the adoption of criteria and the rational use of PMs need to be adopted to avoid dangerous effects on the environment, such as plant toxicity and water contamination. The potentialities and risks of SM applications are discussed in this chapter.
Part of the book: Soil Contamination
Machine Learning, Compositional and Fractal Models to Diagnose Soil Quality and Plant Nutrition
Soils, nutrients and other factors support human food production. The loss of high-quality soils and readily minable nutrient sources pose a great challenge to present-day agriculture. A comprehensive scheme is required to make wise decisions on system’s sustainability and minimize the risk of crop failure. Soil quality provides useful indicators of its chemical, physical and biological status. Tools of precision agriculture and high-throughput technologies allow acquiring numerous soil and plant data at affordable costs in the perspective of customizing recommendations. Large and diversified datasets must be acquired uniformly among stakeholders to diagnose soil quality and plant nutrition at local scale, compare side-by-side defective and successful cases, implement trustful practices and reach high resource-use efficiency. Machine learning methods can combine numerous edaphic, managerial and climatic yield-impacting factors to conduct nutrient diagnosis and manage nutrients at local scale where factors interact. Compositional data analysis are tools to run numerical analyses on interacting components. Fractal models can describe aggregate stability tied to soil conservation practices and return site-specific indicators for decomposition rates of organic matter in relation to soil tillage and management. This chapter reports on machine learning, compositional and fractal models to support wise decisions on crop fertilization and soil conservation practices.
Part of the book: Soil Science