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Abstract Although Texas is not widely known for organic production, it is responsible for most organic peanut production in the United States. When managed effectively, this can be a lucrative practice due to consumer demands. However, farmers pursuing organic management must undergo a 3‐year transition period to obtain United States Department of Agriculture certification, which is often associated with depressed yields. The objective of this study was to measure the impact of organic management and cover crop selection on biological indicators of soil health during the second year of transitioning to an organic cotton–peanut rotation in West Texas. We conducted a field study at two locations, and soil health indicators including in situ respiration, carbon mineralization, enzyme activities, and phospholipid fatty acids (PLFAs) were measured. The use of a cover crop resulted in generally greater biological activities compared to the fallow after only one cover crop rotation, representing early elevated biological activities under plots treated with a cover crop. However, cover crop species selection typically does not affect soil biological parameters. Cover crop use also did not contribute to yield losses when compared to a fallow treatment. When comparing organic management to conventional management, both sites experienced elevated PLFA counts under organic management. Importantly, results indicated that although reduced peanut yields could be expected under transitional organic management, this was not cover crop species‐specific, nor was it a guarantee. Given the economic risk of organic management for farmers, this study will benefit producers deciding whether to pursue organic production and cover cropping.

Abstract Commercial cultivars with advanced technology have reduced pest pressures, while greater seed costs have increased total production cost. Limited information is available on the optimal final population density (PD) for the commercially available cotton (Gossypium hirsutum L.) cultivars with advanced technologies in water‐scarce environments. Therefore, our objectives were to examine the effects of PD on cotton growth and development, lint yield, fiber quality, and net return. A 2‐year study was conducted to test four PDs (low, medium, high, and very high) in deficit‐irrigated and dryland conditions at Chillicothe, TX. Final PD at 12 days after planting were 54,078, 109,563, 124,037, and 151,377 plant ha−1 in irrigated and 67,346, 115,335, 116,397, and 145,432 plant ha−1 in dryland trials. Maturity was delayed in the low PD early in the season; however, the differences on maturity ceased toward the end of the season. No statistical differences were observed on lint yield and fiber quality among treatments in irrigated and dryland trials. Average lint yields were 1199 kg ha−1 in irrigated and 796 kg ha−1 in dryland trial. Net returns were similar among all PD in the irrigated trial, while low PD had significantly higher net‐return than very high PD in the dryland trial. The higher net return at the low PD was due to the lower seed cost associated with a low seeding rate. In the water‐scarce environment, final plant density of 54,078 plant ha−1 in deficit irrigation and 67,346 plant ha−1 in dryland produced optimal yield and net return as compared to higher PD examined.

Cotton boll count is an important phenotypic trait that aids in a better understanding of the genetic and physiological mechanisms of cotton growth. Several computer vision technologies are available for cotton boll segmentation. However, estimating the number of cotton bolls in a segmented cluster of cotton bolls is a challenging task due to the complex shapes of cotton bolls. This study proposed a combination of spectral-spatial and supervised machine learning based methods for cotton boll candidate recognition and counting from high resolution RGB images obtained from unmanned aerial vehicles (UAVs). An algorithm consisting of machine vision, band-mean filter, Otsu thresholding, red/blue band ratio filter, and geometrical characteristics-based error removal techniques, was employed to detect open cotton boll pixels under several environmental settings. In addition, a support vector machine (SVM) based encoding method was developed using geometric features of cotton boll candidates to predict the number of cotton bolls from the segmented cotton boll candidates. This algorithm was implemented over three experiment sites with three cotton varieties, two tillage practices, seven cover crop treatments, two irrigation regimes (irrigated and rainfed), 26 irrigation levels, and two sensors (DJI FC6310 RGB and MicaSense Rededge) capturing images at two spatial resolutions (0.75 cm and 1.07 cm) over two growing seasons (2019 and 2021). These different experimental settings allowed the proposed approaches to be validated against a variety of complex backgrounds. A visual inspection of 1000 randomly selected pixels revealed that the proposed cotton boll candidate recognition approach was highly effective in segmenting cotton bolls and background pixels, with high classification accuracy (> 95%) and a low number of falsely classified pixels (precision > 0.96; recall > 0.93). A high correlation between ground truth observations and predicted cotton boll count indicated that the use of geometric features of segmented candidates as predictors in association with the SVM model demonstrated a good performance in estimating boll count from recognized cotton boll candidates. Furthermore, linear regression analyses revealed that both boll count and candidate area are potential predictors of lint yield, with boll count being a better predictor than candidate area. Overall, the study demonstrated that machine vision/learning techniques can be potentially used on UAV images to count the number of cotton bolls and predict lint yield over large acreages with reasonable accuracy.

Increasing agriculture intensification to meet global demand has sparked great interest in economic and environmentally sustainable cropping systems. One important measure of environmental sustainability is greenhouse gases production, which can be altered with soil health promoting practices such as cover cropping and no-tillage. Previously, the measurement of gas concentration and production in soils has been cost-prohibitive for factorial designs in agricultural research and may inhibit field operations. This study aimed to provide an affordable method for measuring below ground pore space concentrations of greenhouse gases. Testing was conducted in Lubbock, TX in 2019 in a cotton monoculture with gas sampling conducted about a month after fertilizer applications. Probes were installed at 7.5 and 15 cm depths and sampling was conducted with a portable Fourier-transform infrared gas analyzer. It was determined that consistent values of carbon dioxide (CO2) concentration were measured with average CO2 concentrations across tillage systems of 570 µL L−1 at 7.5 cm and 610 µL L−1 at 15 cm in June and 1050 µL L−1 at 7.5 cm and 1380 µL L−1 at 15 cm in August. Compared to pore space concentrations from similar soil moisture conditions, our measurements were highly comparable. Soil pore space probes used in this study provided comparable measurement of soil gas concentrations at a low per-plot cost. In addition, our probes were able to be installed with minimal soil disturbance allowing for measurement in already established no-tillage systems and allowed for field operations to be conducted by placing probes within the crop row.

Core Ideas Negative impacts of fresh wheat residue on canola were demonstrated in the lab. No negative impacts were observed in pot conditions that mimicked the field. Results were generally consistent across 15 wheat varieties and 2 soils. Negative impacts of wheat residue reported on canola are likely physical impacts. Wheat (Triticum aestivum L.) has been studied for its residual chemical effects on other crop plants, which are often cultivar‐specific, but its effects on canola (Brassica napus L.), a crop increasingly rotated with wheat, are less understood. Because canola production is spreading to new areas, including the US Southern Great Plains, our objective was to evaluate the potential for allelopathic properties of wheat residue from regional cultivars to negatively impact emergence and early growth of canola. A laboratory study was conducted, establishing a baseline for potential allelopathic effects, and an outdoor pot study was conducted, designed to test for these effects in conditions mimicking field conditions and crop management (e.g., residue weathered on the soil surface for the summer to simulate fallow). In the laboratory, with exposure to fresh wheat residue extracts, canola germination was delayed, but persistent, concentration‐dependent negative effects on radicle elongation were observed, similar to other reported results. In the pot studies, wheat residue did not affect canola emergence, with minimal variation among 15 wheat cultivars tested in two soils, and early growth increased by an average of 23% with residue treatment. The canola emergence and early growth results indicated an absence of allelopathy at planting and the presence of growth‐promoting factors following wheat residue application. Together, these studies indicate that allelopathic properties of wheat residue can negatively impact canola in laboratory conditions, but suggests these effects are not likely to be realized under typical field conditions. Negative impacts of wheat residue on canola, which have been reported, are likely physical in nature.