Increasing Sustainability Of Agricultural Systems Through Adaptive Crop Management Practices And Technologies

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Increasing Sustainability Of Agricultural Systems Through Adaptive Crop Management Practices And Technologies

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Cover crops can provide ecological services and improve the resiliency of annual cropping systems; however, cover crop use is low in corn (Zea mays L.)-soybean [Glycine max (L.) Merr.] rotations in the upper Midwest due to challenges with establishment. Our objective was to compare three methods to establish five cover crops in corn at the seven leaf collar stage. Establishment methods included directed broadcast of seed into the inter-row (DBC), directed broadcast with light incorporation (DBC+INC), and a high-clearance drill (DRILL). Fall cover crop biomass was greater with the DRILL method than DBC for all cover crops except pennycress, and the DRILL and DBC+INC methods resulted in greater spring biomass for red clover and hairy vetch than DBC. Cover crop biomass and N uptake in the spring was among the greatest with winter rye (means = 971 kg DM ha-1 and 25 kg N ha-1, respectively). Cover crop treatments did not affect corn grain or silage yield, and reduced seed yield of the subsequent soybean crop by 0.4 Mg ha-1 (10%) only when poor termination of hairy vetch occurred at Lamberton. Soil nitrate N was reduced by winter rye at both locations and by hairy vetch, red clover, and pennycress at Waseca, compared to the no cover control. These results demonstrate that cover crops can be interseeded into corn at the seven leaf collar stage in the upper Midwest to reduce residual soil nitrate N while maintaining corn and subsequent soybean yields; however; appropriate timing and method of cover crop termination is critical to avoid competition with the subsequent soybean crop. Winterkill of alfalfa (Medicago sativa L.) causes substantial yield losses in northern environments, requiring alternative forages to meet livestock needs. This study explores the forage crop yield, nutritive value and N response of seven annual forage species and one grass-legume biculture, no-till planted into spring-terminated alfalfa. Forages were planted in late-May with split-plot factors of three N fertilizer rates (0, 56, and 112 kg N ha-1) and were harvested on approximately 30-d intervals. When successfully established, teff [Eragrotis tef (Zuccagni) ‘Summer Lovegrass’] and sudangrass [Sorghum bicolor (L.) subsp. drummondii (Nees ex Steud.) ‘PCS 3010’] were among the highest-yielding species, with yields ranging from 4.2 to 9.9 Mg DM ha-1 and 6.8 to 8.9 Mg DM ha-1, respectively. Fertilizer N increased yields of all species at Rosemount in 2014; however, N needs were met by terminated alfalfa at both locations in 2015. Weed biomass increased with the addition of fertilizer N in site-years when weeds were present. Nitrogen fertilization did improve forage nutritive value through decreased neutral detergent fiber concentration and increased crude protein concentration and neutral detergent fiber digestibility (48-hr in-vitro) in all site-years. However, N fertilization had no effect on economic net return in two of three site-years. Annual ryegrass [Lolium multiflorum (Lam.) ‘Jumbo’] most consistently resulted in the greatest net return. No-till planting annual forages into terminated alfalfa can provide forage to offset losses and utilize alfalfa N in situations of alfalfa winterkill. In-field estimations of alfalfa yield and nutritive value can inform management decisions to optimize forage quality and production. However, acquisition of timely information at the field scale is limited using traditional measurements such as destructive sampling and assessment of plant maturity. Remote sensing technologies (e.g. measurement of canopy reflectance) have the potential to enable rapid measurements at the field scale. Canopy reflectance (350‐2500 nm) and LiDAR-estimated canopy height were measured in conjunction with destructive sampling of alfalfa across a range of maturity at Rosemount, MN in 2014 and 2015. The full range of reflectance data was processed with stepwise regression using the Bayesian Information Criterion to identify individual wavebands most correlated with alfalfa nutritive value. Models were reduced by spectral range and number of wavebands to improve model utility., and cumulative Growing Degree Units (GDUs) and canopy height were added as predictors. Optimum predictions of R2 = 0.89, 0.91, 0.89, 0.87 for yield, crude protein, neutral detergent fiber, and neutral detergent fiber digestibility (48-hr in-vitro). This research establishes potential for remote sensing measurements to be integrated with environmental information to achieve rapid and accurate predictions of alfalfa yield and nutritive value at the field scale for optimized harvest management.


University of Minnesota Ph.D. dissertation. June 2017. Major: Applied Plant Sciences. Advisors: Michael Wells, Jeffrey Coulter. 1 computer file (PDF); vi, 92 pages.

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Noland, Reagan. (2017). Increasing Sustainability Of Agricultural Systems Through Adaptive Crop Management Practices And Technologies. Retrieved from the University Digital Conservancy,

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