Abstract:
Measuring and improving resiliency is the key goal of climate risk management in rainfed
agriculture. Currently, available metrics are generally used to qualitatively measure the resilience
of agricultural systems at broader scales. Moreover, these metrics showed non-linear responses to
climate variability, thus often fail to capture the temporal dynamics of resilience at field scales.
Our objective for this study is to combine a few soil moisture-based metrics to gauge the
resiliency of three promising rainfed agricultural treatments, namely the no-till, organic, and
reduced input treatments against the conventional treatment. These treatments have been
established at the Kellogg Biological Station Long-Term Ecological Research (KBS-LTER)
experiment in a randomized complete block design with six blocks per treatment. All four of
these treatments consisted of maize (Zea mays)-soybean (Glycine max)-winter wheat (Triticum
aestivum) in rotation. Long-term (1993–2018) soil moisture data from this experiment was
collected to compute the soil moisture metrics while the total crop biomass, crop yield, and soil
organic carbon data were statistically analyzed to evaluate the robustness of the metrics to gauge
the resiliency of these systems. Results have shown that, among the soil moisture metrics, the
mean relative difference, the Spearman’s rank correlation coefficient, and soil water deficit index
were suitable, while the index of temporal stability was not suitable to gauge the resiliency of
different rainfed agricultural systems. The no-till treatment was identified as the most resilient
treatment in terms of soil moisture retention, effectiveness for drought mitigation, and crop
yields. Meanwhile, the reduced input treatment was the least resilient in terms of soil water
conservation and drought recovery. The results of this study can be extended to other Midwest
regions of the United States and similar climatological areas around the world.
