Maintaining SH (SH) is critical for sustainable field crop production. The first step to understanding drivers is evaluating the effects of climate, soil edaphic properties, and management practices from an on-farm study across a regional scale on SH. Thus, I integrated on-farm research, farmer participatory approach, and statistical analysis to understand the SH drivers in the United States and Malawi. In summary, I assessed the SH indicators through two perspectives with various statistical models: 1) understand various viewpoints on SH assessment; and 2) integrating Bayesian statistical analysis, hierarchical cluster analysis, and principal component analysis to determine the drivers of SH and yield in Michigan, U.S. and the Central and Southern region of Malawi.SH is assessed through soil physical, chemical, and biological properties. However, researchers used various minimum laboratory SH dataset, resulting in the inconsistency in research studies. On-site SH evaluations recommended by extension educators were not always adopted by farmers. In this chapter, we compared the common indicators in laboratory analysis and on-farm evaluation through literature review and used a Likert Scale to understand farmers’ views in Michigan. Maintaining SH (SH) is critical for sustainable field crop production. The first step to understanding drivers is evaluating the effects of climate, soil edaphic properties, and management practices from an on-farm study across a regional scale on SH. Thus, the farmer participatory approach and statistical analysis were integrated to understand the SH drivers in the United States and Malawi. In summary, SH indicators were assessed in this study through two perspectives with various statistical models: 1) understand various viewpoints on SH assessment; and 2) integrating Bayesian statistical analysis, hierarchical cluster analysis, and principal component analysis to determine the drivers of SH and yield in Michigan, U.S. and the Central and Southern region of Malawi.SH is assessed through soil physical, chemical, and biological properties. However, researchers used various minimum laboratory SH datasets, resulting in inconsistency in research studies. On-site SH evaluations recommended by extension educators were not always adopted by farmers. In Chapter 1, a Likert study was employed to understand farmers’ views of common soil health indicators in Michigan. The results showed that the concept of SH assessment should be consistent and clear in research studies and extension education materials.Soil degradation is the most challenging yield-limiting factor in Sub-Saharan Africa. Without the information of current soil carbon status, farmers do not have sufficient information for deciding the appropriate management practice. Malawi's rain-fed maize system is a representation of the rain-fed maize cropping system in East Africa. In Chapter 2, soil analyses were conducted on 1108 focal plots in Central and Southern Malawi to better understand the current total and labile soil carbon status. Bayesian statistical approaches were employed to evaluate environmental and management drivers for soil total and labile carbon on Malawi smallholder fields. Overall, clay content and the vegetative cover are positive drivers for soil total and labile carbon.To better understand the SH across the regional scale in the Midwest United States, an on-farm study of 242 focal plots was conducted in Michigan. In Chapter 3, participatory monitoring and Bayesian linear regression models were used to investigate the impact of various drivers on SH indicators under a range of conditions in the state of Michigan. Location effects were observed, with each of the three regions differing in their climate, soil edaphic properties, and management practices. Overall, climate and soil edaphic properties were the dominant drivers of SH, management practices, which also play a critical role, especially in enhancing soil biological indicators.When evaluating SH, multivariate statistical analysis is generally used due to the inherent correlation among the variables. In Chapter 4, hierarchical cluster analysis and principal component analysis were adopted to evaluate the 1) interrelationship of various SH indicators; and 2) drivers of the variation across focal plots and local clusters. Besides the high correlated SH indicators, independent variables provide valuable information. The key determinant of SH indicators is geographical clusters. Farmers' management practices should be site-specific and goal-oriented considering the tradeoff between residual nitrogen and soybean yield.
Read
