DEVELOPING ACCURATE SPATIAL MAPS OF COTTON FIBER QUALITY PARAMETERS

Awareness of the importance of cotton fiber quality (Gossypium, L. sps.) has increased as advances in spinning technology require better quality cotton fiber. Recent advances in geospatial information sciences allow an improved ability to study the extent and causes of spatial variability in fiber parameters. However, these studies are often harvested by hand and ginned on small research gins. Fiber quality from cotton lint harvested and ginned in this manner is different from that machine-harvested and ginned on production-scale equipment. The objective of this study was to develop a method of correcting for error introduced into cotton fiber quality parameters from samples as a result of harvest and ginning methods. This correction method will allow more realistic comparisons between results that researchers commonly report and measurements that a producer would receive. Field-grown cotton was harvested either by machine or hand, and ginned on a small research gin or a production-scale gin. The results reported here examine the population characteristics for physiological fiber parameters including micronaire, strength, length and uniformity. The correction needed for translating the research results to the production scale was determined. The error inherent in that correction was determined for different populations of cotton fibers from different years. To demonstrate the impact of the research-induced error and the correction factor, spatial maps of cotton fiber length are plotted

Measuring Soil Electrical Conductivity to Delineate Zones of Variability in Production Fields

Production fields in southeast Kansas are highly variable. Differences in elevation and changes in soil texture contribute to unevenness in plant-available moisture and nutrients, resulting in significant inconsistencies in crop production and yield within a field. These variabilities complicate management and impact the return on investments from different areas of the field. Identification of the regions of variability is possible through several methods, including visual inspection, remote imagery, and yield maps. An additional method of assessing soil variability is by measuring the electrical conductivity of the soil. Measuring apparent electrical conductivity gives a map of the spatial distribution of soil properties, which can be used to identify potential limitations to production and develop site-specific management. Delineation of within-field variability can be used to target production inputs to better match potential crop yield with inputs to maximize return on investment

Hydrologic and Nutrient Modeling within an Agricultural Watershed in Southeast Kansas

Access to safe, clean water is important to support society. Agricultural watersheds are often contaminated due to agricultural activities. Identification of specific factors contributing to impairment of water bodies is important to target remediation efforts. This research is designed to explore water quality within the Middle Neosho Watershed in southeastern Kansas to make more informed decisions in potential corrective actions

Wheat Production

Wheat production in southeast Kansas is often limited due to high rainfall during the harvest. In some years, this high rainfall can exacerbate disease pressure, especially fungal infections. This study presents results from a test of fungicide applications to control Fusarium head blight (FHB) or scab in poor quality wheat

2015 Crop Performance in Southeast Kansas

Crop variety testing determines the production potential of newly released crop cultivars in Southeast Kansas. The genetic potential is moderated by environmental conditions during the growing season as well as soil productive capacity

Evaluating Multi-Species Cover Crops for Forage Production

Cover crops offer potential benefits for improving soil health, but establishment and management costs can be expensive. One way for farmers to recover these costs is to graze the forage, which benefits producers by integrating crop and animal production. More information is needed on the potential forage quantity and quality for grazing livestock of cover crops and mixed species of cover crops. Researchers have suggested that different plant species complement each other, but additional work is needed to determine how best to balance forage production and how competitive the various species are when added to a mix. Sixteen treatments were drill-seeded at the Southeast Research and Extension Center near Columbus, Kansas, in August 2014 and 2015. Each treatment consisted of a three-way mix representing popular cover crops from the plant families Brassicaceae (brassicas), Poaceae (grasses), and Fabaceae (legumes). Eight species were planted, including forage radish (Raphanus sativus), purple-top turnip (Brassica rapa), oat (Avena sativa), rye (Secale cereale), barley (Hordeum vulgare), wheat (Triticum aestivum), Austrian winter pea (Pisum sativum subsp. arvense), and berseem clover (Trifolium alexandrinum). Small areas of each plot were clipped at 45-, 74-, and 91-day intervals each year. The clipped biomass was then weighed, sorted, and dried to determine biomass as well as species composition. In 2014 the average biomass produced at 45, 74, and 91 days was 1,250, 3,290, and 3,050 lb/ac, respectively. These range from 470–1,940 lb/ac 45 days after planting to 1,790–4,440 lb/ac at 91 days after planting, depending on the cover crop mix. In 2015, the average biomass at 45, 74, and 91 days was 1,120, 1,604, and 2,273 lb/ac, respectively. These range from 557-1,876 lb/ ac 45 days after planting to 1,100–4,127 lb/ac at 91 days after planting, depending on the cover crop mix

2015 Soybean Production in Southeast Kansas

Crop performance and yield varies as a function of the growing environment and soil properties within the field. Optimal soybean planting in southeast Kansas usually occurs from mid-May to mid-June for full-season or late-June to early-July for doublecropped soybean. Planting is timed to capture fall rains and cooler temperatures during critical periods of bean development and yield formation and avoid mid-summer heat and drought. Changing planting configuration (row spacing and plant population), timing of planting, and cultivar selection are methods of optimizing soybean production for different growing environments

Crop Production Summary, Southeast Kansas – 2016

Crop production in southeast Kansas is summarized from variety trials and research plot experiments conducted at the Southeast Research and Extension Center fields in 2016

Key Components of Healthy Soils and Their Role in Crop Production

Soil health is a confusing term that means different things to different people. To a crop producer, healthy soils are critical for good crop growth and yield. Some soil properties include soil texture, such as the relative percentage of sand, silt and clay; the water content; nutrient levels; organic carbon content; the microbial community; and microbial activity. These properties are determinants of soil health. Our research confirmed that changes in soil management affect the composition and activity of soil microorganisms in surface soils. Greater concentrations of microbial biomass and arbuscular mycorrhizal fungus (AMF) in the no-till agricultural system indicated healthier soils in this system. Our research also indicated microbial properties in subsurface soils were determined by parent materials and weathering

Improving Resilience of Corn to Weather through Improved Fertilizer Efficiency

Fertilization is a critical management tool to improve crop productivity. Corn requires more N fertilizer than some other crops, but the fertility needs of the crop vary based on the growing environment. In this study, we used a modeling approach to examine the historical record and delineate the interaction between fertilizer and weather on the sensitivity of corn yield to climate in southeastern Kansas. Providing optimal fertilizer can improve corn yield. However, too much fertilizer can be expensive and wasteful. This study demonstrated that the climate resilience of corn is moderated by how much fertilizer is applied. The model results concluded that the optimal N fertilizer rate should be adjusted based on weather conditions