Proceedings
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| Filter results8 paper(s) found. |
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1. NDVI 'Depression' In Pastures Following GrazingPasture biomass estimation from normalized difference vegetation index (NDVI) using ground, air or space borne sensors is becoming more widely used in precision agriculture. Proximal active optical sensors (AOS) have the potential to eliminate the confounding effects of path radiance and target illumination conditions typically encountered using passive sensors. Any algorithm that infers the green fraction of pasture from NDVI must factor in plant morphology and live/dead plant ratio, irrespective... J.S. Stanley, D.W. Lamb, M.G. Trotter, M.M. Rahman |
2. Modifying the University of Missouri Corn Canopy Sensor Algorithm Using Soil and Weather InformationCorn production across the U.S. Corn belt can be often limited by the loss of nitrogen (N) due to leaching, volatilization and denitrification. The use of canopy sensors for making in-season N fertilizer applications has been proven effective in matching plant N requirements with periods of rapid N uptake (V7-V11), reducing the amount of N lost to these processes. However, N recommendation algorithms used in conjunction with canopy sensor measurements have not proven accurate in making N recommendations... G. Bean, N.R. Kitchen, D.W. Franzen, R.J. Miles, C. Ransom, P. Scharf, J. Camberato, P. Carter, R.B. Ferguson, F. Fernandez, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan |
3. Use of a Cropping System Model for Soil-specific Optimization of Limited WaterIn the arena of modern agriculture, system models capable of simulating the complex interactions of all the relevant processes in the soil-water-plant- atmosphere continuum are widely accepted as potential tools for decision support to optimize crop inputs of water to achieve location specific yield potential while minimizing environmental (soil and water resources) impacts. In a recent study, we calibrated, validated, and applied the CERES-Maize v4.0 model for simulating limited-water irrigation... L.R. Ahuja, S.A. Saseendran, L. Ma, D.C. Nielsen, T.J. Trout, A.A. Andales, N.C. Hansen |
4. Thermal Characterization and Spatial Analysis of Water Stress in Cotton (Gossypium Hirsutum L.) and Phytochemical Composition Related to Water Stress in Soybean (Glycine Max)Studies were designed to explore spatial relationships of water and/or heat stress in cotton and soybeans and to assess factors that may influence yield potential. Investigations focused on detecting the onset of water/heat stress in row crops using thermal and multispectral imagery with ancillary physicochemical data such as soil moisture status and photosynthetic pigment concentrations. One cotton field with gradations in soil texture showed distinct patterns in thermal imagery, matching patterns... S.J. Thomson, S.L. Defauw, P.J. English, J.E. Hanks, D.K. Fisher, P.N. Foster, P.V. Zimba |
5. Soil Spatial Variability Assessment and Precision Nutrient Management in Maize (Zea Mays L.)Investigations on soil spatial variability and precision nutrient management based targeted yield approach in maize was carried out at Agricultural research station (ARS), Mudhol (Karnataka), India under irrigated condition during 2013-14, 2014-15 and 2015-16. ARS, Mudhol is located in northern dry zone of Karnataka at 160 20! N latitude, 750 15! E longitude and at an altitude of 577.6 meter above mean sea level. To assess the spatial variability, the study area was divided into 20 x20 m size... M.P. Potdar, G.B. Balol, S.A. Satyareddi, B.T. Nadagouda , C.P. Chandrashekar |
6. Variability in Yield Response of Maize to N, P and K Fertilization Towards Site-specific Nutrient Recommendations in Two Maize Belts in TogoSavannah and central regions are the major maize production zones in Togo, but with maize grain yields at a threshold of only 1.5 Mg ha-1. We use a participatory approach to assess the importance of the major three macro elements (N, P and K) for maize cropping in the two regions in order to further allow for site-specific and scalable fertilizer recommendations. Thirty farmers’ fields served as pilot sites, allocated within the two regions to account for spatial variability in... J.M. Sogbedji, M. Lare, A.K. Lotsi, K.A. Amouzou, T. Agneroh |
7. Evaluating a Satellite Remote Sensing and Calibration Strip-based Precision Nitrogen Management Strategy for Corn in Minnesota and IndianaPrecision nitrogen (N) management (PNM) aims to match N supply with crop N demand in both space and time and has the potential to improve N use efficiency (NUE), increase farmer profitability, and reduce N losses and negative environmental impacts. However, current PNM adoption rate is still quite low. A remote sensing and calibration strip-based PNM strategy (RS-CS-PNM) has been developed by the Precision Agriculture Center at the University of Minnesota.... K. Mizuta, Y. Miao, A.C. Morales, L.N. Lacerda, D. Cammarano, R.L. Nielsen, R. Gunzenhauser, K. Kuehner, S. Wakahara, J.A. Coulter, D.J. Mulla, D. . Quinn, B. Mcartor |
8. Response of Canola and Wheat to Application of Enhanced Efficiency Nitrogen Fertilizers on Contrasting Management ZonesInvestment on nitrogen (N) fertilizers is a major cost of growers, and variable rate (VR) application of N fertilizers could help optimize its usage. In the growing season of 2023, field experiments were conducted at four sites (i.e., Watrous – Saskatchewan SK and two fields in the vicinity of Strathmore, Alberta AB, Canada). The main objectives were to (i) determine performance of Enhanced Efficiency N Fertilizers - EENF (i.e., Coated urea, urea with double inhibitors - DI, urea mixed with... H. Asgedom, G. Hehar, C. Willness, W. Anderson, H. Duddu, P. Mooleki, J. Schoenau, M. Khakbazan, R. Lemke, E. derdall, J. Shang, K. Liu, J. Sulik, E. Karppinen, I. Mbakwe |