Automation continues to play a greater role in agricultural production with commercial systems now available for machine vision identification of weeds and other pests, autonomous weed control, and robotic harvesters for fruits and vegetables. The growing availability of autonomous machines in agriculture indicates that there are opportunities to increase automation in cotton production. This article considers how current and future advances in automation has, could, or will impact cotton production practices. The results are organized to follow the cotton production process from land preparation to planting to within season management through harvesting and ginning. For each step, current and potential opportunities to automate processes are discussed. Specific examples include advances in automated weed control and progress made in the use of robotic systems for cotton harvesting.
Abstract. Cotton post-harvest processing research requires moisture content determination for seed cotton, cottonseed, and lint. Methods for determining moisture content have changed and are no longer consistent between laboratories. This research compared standard procedures documented in 1972 and those currently practiced for finding moisture content by oven drying, and quantified the variability. Seed cotton from four modern cultivars (ranging from 9.4% to 36.8% foreign matter), lint, and cottonseed were brought from dry conditions, blended, and then stored for more than 30 days in a controlled environment (21°C, 65% RH) to reach uniform moisture content. Additionally, 150 seed cotton samples were placed in plastic zipper bags and sent by air freight to a distant location and back or stored on-site. Drying baskets (652 cm3) were loaded, in random order, with 25, 35, 50, 71, and 100 g seed cotton or 10, 14, 20, 28, and 40 g lint. Cottonseed was placed in 45 cm3 aluminum cups (10 g) or 800 cm3 aluminum baskets (50 g). Wet weights were determined in the controlled environment. After drying, replicated sets of seed cotton, lint, and cottonseed samples were weighed inside a drying oven and then outside the oven while still hot. Some samples were dried for twice the recommended duration. Sample location in the ovens was tracked. Weighing hot seed cotton samples outside the oven after drying increased apparent moisture content by approximately 0.5% due to air buoyancy; weighing lint samples outside the oven increased apparent moisture content by 1%. Smaller differences in apparent seed cotton moisture content were found when halving or doubling the amount of material in drying baskets or doubling the drying duration. Foreign matter had a minor influence on apparent moisture content. Storage for three days and shipping by air freight in plastic zipper bags did not measurably change the apparent moisture content of seed cotton. Sample location within the drying oven made no difference. Current practices are satisfactory if dry weight location is taken into consideration. Measurement uncertainty has decreased compared to 50 years ago, but the recommended minimum number of samples per treatment was increased slightly for greater statistical power. Keywords: Cotton lint, Cottonseed, Moisture content, Oven drying, Seed cotton.
Combining ability describes the breeding value of parental lines to produce hybrids. The objectives of this study were to estimate specific (SCA) and general combining ability (GCA) for ginning rate and net ginning energy requirement in a set of upland cotton (Gossypium hirsutum L.) germplasm. Ginning efficiency was based on measurements of ginning energy (watt hour [Wh] kg−1 lint) and ginning rate (g lint s−1). There is little information in the scientific literature on this topic. Crosses were made in a 5 × 8 factorial design between five female and eight male diverse cotton genotypes. The experiment was conducted in a randomized complete plot design. Analysis of variance was performed using a mixed model of the GLM procedures. Even though the study indicated the presence of both additive (GCA) and nonadditive (SCA) genes controlling these traits, a preponderance of the GCA effects was evident. The cotton genotype MD 25 had the highest GCA effect for ginning rate, and AR 9317‐26 had the highest significant negative GCA effect for net ginning energy. For lowering fuzz percentage, any of the seminaked seed lines (AR 9317‐26, Tejas NS, SC 9023 NS) can be exploited. This study and other previous studies showed significant positive correlation of fuzz percentage with net ginning energy. For cultivar development, cross JJ 1145ne × SC 9023 NS may be used for improving ginning rate, crosses FM 832 × Tejas NS and JJ 1145ne × SG 747 can be used for reducing fuzz percentage, and the hybrid Phytogen 72 × TAM 98‐99ne can be exploited for improving all three traits.
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