Location: Crop Production Systems Research
2022 Annual Report
Objectives
1. Optimize early soybean production system and associated pest management
strategies for the Mid-Southern United States.
2. Develop innovative cotton management approaches that will optimize physiological responses of the cotton plant to environmental factors so that it can make the most efficient use of production inputs to improve lint yield and fiber quality.
2.1. Quantify yield, fiber quality, growth and development for varying cotton plant population densities with adequate and less-than-adequate N fertilization, and under irrigated or dryland production.
2.2. Quantify yield, fiber quality, growth and development for varying cotton varieties grown in both twin-row and single-row planting patterns under irrigated or dryland production.
2.3. Assess benefits of transgenic and non-transgenic cotton-soybean rotation systems on soil properties, weeds, yield, and seed and fiber quality in the
Mississippi Delta.
3. Assess the benefits of new drought tolerant, multiple herbicideresistant, and insect-resistant (stacked gene traits) in current or new production systems.
4. Assess impacts of transgene and glyphosate applications on soil microbial communities, plant-microbe interactions, as well as plant health and productivity in corn and soybean.
5. Identify new and/or alternative crops for the Mid-South, determine their potential, and develop management strategies for integration and production.
Approach
The purpose of this project is to develop productive, profitable, and sustainable crop production systems for three of the mid-southern major row crops (soybean, cotton, and corn) that increase yield, improve quality, and reduce production costs.
Over the next five years, we will conduct customer-driven basic and applied
research aimed at improving regional-specific cropping systems that are profitable, conserve natural resources, provide effective pest control, and make efficient use of production inputs. The specific production practices to be researched in these 3 major crops include row patterns and row spacing, seeding rates, new genotypes, nutrient management, crop rotations, irrigation, planting dates, and transgene and glyphosate effects on plant health and productivity of corn and soybean. In addition, alternative crops that could be produced using existing equipment and fit into rotation systems will be researched.
Progress Report
This is a brigding project 6066-22000-089-000D pending postponement of NP 305 research review. Please refer to project #6066-22000-082-000D final report for more info.
Accomplishments
Review Publications
Cho, J.B., Guinness, J., Kharel, T.P., Maresma, Á., Czymmek, K.J., Van Aardt, J., Ketterings, Q.M. 2021. Proposed method for statistical snalysis of on-farm single strip treatment trials. Agronomy. 11(10):2042. https://doi.org/10.3390/agronomy11102042.
Kharel, T.P., Ashworth, A.J., Owens, P.R. 2022. Linking and sharing technology: Partnerships for data innovations for management of agricultural big data. Data. https://doi.org/10.3390/data7020012.
Zhang, T., Huang, Y., Reddy, K.N., Yang, P., Zhang, J. 2021. Using machine learning and hyperspectral images to assess damages to corn plant caused by glyphosate and to evaluate recoverability. Agronomy. 11:583. https://doi.org/10.3390/agronomy11030583.
Dhakal, M., Huang, Y., Locke, M.A., Reddy, K.N., Moore, M.T., Krutz, J., Gholson, D., Bajgain, R. 2022. Assessment of cotton and sorghum stand establishment using UAV-based multispectral and DSLR-based RGB imagery. Agrosystems, Geosciences & Environment. 5(2):e20247. https://doi.org/10.1002/agg2.20247.
Quintana-Ashwell, N., Anapalli, S.S., Pinnamaneni, S.R., Kaur, G., Reddy, K.N., Fisher, D.K. 2021. Profitability of twin-row planting and skip-row irrigations in a humid climate. Agronomy Journal. 114(2):1209-1219. https://doi.org/10.1002/agj2.20847.
Kharel, T.P., Ashworth, A.J., Owens, P.R., Philipp, D., Thomas, A.L., Sauer, T.J. 2021. Teasing apart silvopasture system components using machine learning for optimization. Soil Systems. https://doi.org/10.3390/soilsystems5030041.
Huang, Y., Zhao, X., Pan, Z., Reddy, K.N., Zhang, J. 2022. Hyperspectral plant sensing for differentiating Glyphosate-resistant and Glyphosate-susceptible Johnsongrass through machine learning. Pest Management Science. 78:2370-2377. https://doi.org/10.1002/ps.6864.
Joshi, D.R., Ghimire, R., Kharel, T.P., Mishra, U., Clay, S.A. 2021. Conservation agriculture for food security and climate resilience in Nepal. Agronomy Journal. https://doi.org/10.1002/agj2.20830.
Pinnamaneni, S.R., Saseendran, A., Molin, W.T., Reddy, K.N. 2022. Effect of cereal rye cover crop on weed control, soybean yield and profitability. Frontiers in Agronomy. https://doi.org/10.3389/fagro.2022.907507.
Anapalli, S.S., Pinnamaneni, S., Reddy, K.N., Sui, R., Singh, G. 2022. Investigating soybean (Glycine max L.) responses to irrigation on a large-scale farm in the humid climate of the Mississippi Delta region. Agricultural Water Management. 262:107432. https://doi.org/10.1016/j.agwat.2021.107432.
