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United States Department of Agriculture

Agricultural Research Service

Research Project: ADVANCING SUSTAINABLE AND RESILIENT CROPPING SYSTEMS FOR THE SHORT GROWING SEASONS AND COLD, WET SOILS OF THE UPPER MIDWEST

Location: Soil Management Research

Title: Scaling-up from plot to watershed for agro-ecosystem services appraisal under climate change

Authors
item Jaradat, Abdullah
item Starr, Jon
item Boody, George -

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: July 24, 2013
Publication Date: N/A

Technical Abstract: The highly diverse soils in the Chippewa River Watershed (CRW) in west central Minnesota are increasingly the focus of conservation efforts. Land use in CRW has a major impact on the status of soil conservation and on agro-ecosystem services (AES), including provisioning, supporting, regulating and providing cultural services. Field-scale indicators of AES are largely missing in the watershed; proxy-based indicators can help to assess these services under current and predicted global climate change (GCC). We developed and validated partial least squares regression models for two major Mollisols within CRW on the basis of cropping systems-crop rotations-crops-soils-weather variables in a long-term field experiment, then scaled-up to simulate the impact of 100 years each of current weather (A0) and projected A2 GCC scenario on provisioning and regulating AES. We developed individual indices for biomass and grain yields, nitrate- and ammonium-nitrogen, soil carbon, runoff and soil erosion for 132 soil series representing ~90% of the land area in CRW. These indicators and a weighted index based on their relative importance were subjected to distance-weighted least squares, multivariate, and variance components analyses to develop 2-dimensional (2-D) maps of CRW, identify sources of variation and quantify the relative impact of each source of variation on these indicators. The 2-D maps delineated contiguous areas of increasing or decreasing AES in response to projected GCC and its interaction with several management factors. The largest and most significant variance portions in the weighted index were attributed to differences between GCC scenarios (A0 and A2); they were followed by differences attributed to the interaction of crop rotations with land capability classes (LCC) within conventional and organic cropping systems. The weighted index was predicted (R2=0.96; p<0.000) and validated (Q2=0.93; p<0.0001) with high certainty. The prediction and validation models were positively dominated by the projected GCC scenario (A2), organic cropping system, and crop rotations of increased complexity (i.e., those with 2 to 3 years of perennial forage legume followed by traditional small grains and when conducted on the most productive land capability class, LCC-1). Crop rotations were correctly classified (range from 68 to 99%) on the basis of multiple AES indicators. Multivariate distances between crop rotations increased with their complexity and with their AES indicators. A gradual increase in the soil-nitrogen reserve due to increased frequency of a forage legume in the crop rotation was followed by positive AES (biomass and grain yield, and soil carbon, in increasing order); these AES were predicted with large certainty under A2 in organically-managed LCC-1. However, more runoff and soil erosion levels are predicted in conventionally-managed LCC-2 and LCC-3 under the same GCC scenario regardless of soil heterogeneity. The modeling framework and mapped AES indicators are designed to achieve multiple goals through predictive modeling, market development for perennial crops and livestock, and will be used to support farmers in designing LCC- or soil series-specific crop rotation(s) to address sustained delivery of multiple AES while enhancing soil conservation, water quality, and environmental protection aspects of farming in the CRW. This research conducted by NCSCRL is part of a larger initiative called the Chippewa 10% Project, with several partners, that is designed to achieve water quality goals through predictive modeling, outreach to farmers, market development for perennial crops and livestock on the land and monitoring for actual impacts in area streams and soils.

Last Modified: 11/28/2014
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