Dynamics of Mean-Variance-Skewness of Cumulative Crop Yield Impact Temporal Yield Variance

Farmers' decision to adopt new management or production system depends on production risk. Grain yield data was used to assess production risk in a field experiment composed of two cropping systems (CNV and ORG), each with eight subsystems (two levels each of crop rotation (2-yr and 4-yr), tillage management (conventional, CT and strip, ST), and fertilizer input (fertilized, YF and non-fertilized, NF)). Statistical moments, cumulative yield (CY), temporal yield variance (TYV) and coefficient of variation (CV) were used to assess the risk associated with adopting combinations of new management practices in CNV and ORG. The mean-variance-skewness (M-V-S) statistics derived from yield data separated all 16 subsystems into three clusters. Both cropping systems and clustered subsystems differed as to their ability to maintain a constant yield over years, displayed different yield cumulative probabilities, exhibited significant and different M-V-S relationships, and differed as to the reliability of estimating TYV as a function of CY. Results indicated that differences in management among cropping systems and subsystems contributed differently to the goal of achieving yield potential as estimated by the cumulative density function, and that certain low-input management practices caused a positive shift in yield distribution, and may lower TYV and reduce production risk. 1. Introduction Production risk influences farmers’ decision to adopt a new management practice or a production system [1]; therefore, the sustainability of cropping systems is becoming increasingly important to farmers and researchers alike [2]. Although management systems with reduced chemical and energy inputs are of particular interest in assessing sustainability [3, 4], some of them can be less stable than others depending on the cropping system in question [2, 4]. Yield instability, whether caused by spatial and/or temporal variation, can be identified and measured based on performance of long-term experiments [5]. Farmers may not be able to fully manage spatial variation if temporal variation is a strong and recurring factor [6]. Temporal yield variation can be managed to some extent by making the right management decisions at the right time; however, it is equally important for famers, through investment and knowledge, to predict and control this variation [6, 7]. Quantifying treatment main effects in cropping systems experiments provides valuable information that can be augmented by examining the interaction between years and treatment [8] through which cumulative effects of