Recovering soil productivity attributes from experimental data: A statistical method and an application to soil productivity dynamics
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This paper develops a method for deriving information about soil quality trends from a limited related datasets (such as from a long-term crop experiment), for use when a time series of direct measurements is not available. This paper also applies a dynamic statistical estimation method to derive a measure of the generation and level of soil productivity based on a time series data set of crop yields, nutrient inputs, and management techniques. Although the connection between inputs and productivity is well known, this analysis presents new understanding of some of the dynamics of this relationship, such as results indicating that the impact of crop choice on productivity declines over the studied time frame. The authors findings reveal that Nitrogen fertilizer is only a short term substitute for soil productivity; long term soil quality loss due to intensive agriculture cannot be reversed by nitrogen inputs. Regardless of N fertilization rates, continuous corn cultivation rapidly decreases soil productivity. In contrast, rotational cropping with legumes can rapidly regenerate soil productivity. The methods presented in this paper are advantageous because they are applicable with only limited time series data. The findings suggest important venues for further research in international issues of sustainable agriculture.