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Abstract: Sheep (Ovis aries L.) grazing, a cost-effective method of controlling weeds compared to herbicide application and tillage, may influence soil C and N levels by consuming plant residue and returning feces and urine to the soil, but little is known about the practice on soil C and N storage under dryland cropping systems in the northern Great Plains, USA. Three weed control practices [sheep grazing (GRAZ), herbicide application (CHEM), and tillage (MECH)] and three cropping sequences [continuous alfalfa (Medicago sativa L.) (CA), continuous spring wheat (Triticum aestivum L.) (CSW), and spring wheat-pea (Pisum sativum L.)/barley (Hordeum vulgaris L.) hay mixture-fallow (W-P/B-F)] were evaluated on a Blackmore silt loam from 2009 to 2011 in southwestern Montana, USA. Crop yields and soil organic C (SOC), total N (STN), NH4-N, and NO3-N contents at the 0–120cm depth were quantified. Annualized spring wheat grain and biomass (stems+leaves) yields and C and N contents were greater with CSW than with W-P/B-F, but hay biomass and C content were similar between CA and W-P/B-F. While C and N in aboveground biomass after spring wheat and hay harvest were removed through haying in CHEM and MECH, sheep grazing removed about 99% of these elements in GRAZ. The SOC and STN at 5–15cm were greater with CSW or W-P/B-F than with CA in GRAZ and MECH, but SOC at 30–60cm was greater with CA than with CSW in MECH. The NH4-N content at most depths varied among treatments and years, but NO3-N content at 5–120cm was greater with CSW and W-P/B-F than with CA. Longer duration of sheep grazing during fallow periods due to increased return of C and N through feces and urine or residue incorporation to a greater depth probably increased soil C and N storage at the surface layer with CSW and W-P/B-F in GRAZ and MECH, but increased root biomass likely increased C storage at the subsurface layer with CA in MECH. Absence of N fertilization and/or greater N uptake probably reduced soil NO3-N level with CA than with other cropping sequences. Regardless of treatments, SOC and STN declined from 2009 to 2011, probably due to residue removal from haying and grazing. Moderate sheep grazing during fallow periods can be used to increase soil C and N storage, obtain farm C credit, and sustain dryland crop yields compared to herbicide application for weed control in the semiarid regions. [Copyright &y& Elsevier]

Abstract: We used an object-oriented approach in conjunction with the Random Forest algorithm to classify agricultural practices, including tillage (till or no-till (NT)), crop intensity, and grassland-based conservation reserve (CR). The object-oriented approach allowed for per-field classifications and the incorporation of contextual elements in addition to spectral features. Random Forest is a classification tree-based advanced classifier that avoids data over-fitting associated with many tree-based models and incorporates an unbiased internal classification accuracy assessment. Landsat satellite imagery was chosen for its continuous coverage, cost effectiveness, and image accessibility. Classification results for 2007 included producer''s accuracies of 91% for NT and 31% for tillage when applying Random Forest to image objects generated from a May Landsat image. Low classification accuracies likely were attributed to the misclassification of conservation-based tillage practices as NT. Results showed that the binary separation of tillage from NT management is likely not appropriate due to surface spectral and textural similarities between NT and conservation-type tillage practices. Crop and CR lands resulted in producer''s accuracies of 100% and 90%, respectively. Crop and fallow producer''s accuracies were 95% and 82% in the 2007 classification, despite post-senesced vegetation; misclassification within the fallow class was attributed to pixel-mixing problems in areas of narrow (<100 m) strip management. A between-date normalized difference vegetation index approach was successfully used to detect areas having “changed” in vegetation status between the 2007 and prior image dates; classified “changed” objects were then merged with “unchanged” objects to produce crop status maps. Field crop intensity was then determined from the multi-year analysis of generated crop status maps. [Copyright &y& Elsevier]