Applying statistical causal analyses to agricultural conservation: A case study examining P loss impacts

Authors: QIAN, SONG - University Of Toledo; Harmel, Daren

Submitted to: Journal of the American Water Resources Association
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/17/2015
Publication Date: 2/26/2016
Citation: Qian, S.S., Harmel, R.D. 2016. Applying statistical causal analyses to agricultural conservation: A case study examining P loss impacts. Journal of the American Water Resources Association. 52(1):198-208.

Interpretive Summary: Estimating the effect of agricultural conservation practices on reducing nutrient loss using measured data can be complicated by differing crop types and differing management practices. As we may not have the full knowledge of these factors, conventional statistical methods are often ineffective. In this paper, we discuss the use of two alternative methods for quantifying the effects of water and soil conservation practices in reducing phosphorous loss from agricultural fields based on an extensive database with measured data. Both methods resulted in similar estimates of the conservation practice effect of an average reduction in total phosphorus loads of approximately 70%. In addition, both methods show evidence of conservation practices reducing the incremental increase in total phosphorus loss per unit increase in fertilizer application. The methods are applicable for improved assessment of agricultural practices and their effects and can be used for providing realistic parameter values for watershed-scale modeling.

Technical Abstract: Estimating the effect of agricultural conservation practices on reducing nutrient loss using observational data can be confounded by differing crop types and differing management practices. As we may not have the full knowledge of these confounding factors, conventional statistical methods are often ineffective. In this paper, we discuss the use of two statistical causal analysis methods (propensity score and multilevel modeling) for quantifying the effects of water and soil conservation practices in reducing phosphorous loss from agricultural fields. With the propensity score method, a subset of the data was used to form a treatment group and a control group with similar distributions of confounding factors. With the multilevel modeling approach, the data are stratified based on important confounding factors and the conservation practice effect was evaluated for each stratum. Both methods resulted in similar estimates of the conservation practice effect of an average reduction in total phosphorus loads of approximately 70%. In addition, both methods show evidence of conservation practices reducing the incremental increase in total phosphorus loss per unit increase in fertilizer application. The methods are applicable for improved assessment of agricultural practices and their effects and can be used for providing realistic parameter values for watershed-scale modeling.