In the United States, petroleum extraction, refinement, and transportation present countless opportunities for spillage mishaps. A method for rapid field appraisal and mapping of petroleum hydrocarbon-contaminated soils for environmental cleanup purposes would be useful. Visible near-infrared (VisNIR, 350-2500 nm) diffuse reflectance spectroscopy (DRS) is a rapid, nondestructive, proximal-sensing technique that has proven adept at quantifying soil properties in situ. The objective of this study was to determine the prediction accuracy of VisNIR DRS in quantifying petroleum hydrocarbons in contaminated soils. Forty-six soil samples (including both contaminated and reference samples) were collected from six different parishes in Louisiana. Each soil sample was scanned using VisNIR DRS at three combinations of moisture content and pretreatment: (i) field-moist intact aggregates, (ii) air-dried intact aggregates, (iii) and air-dried ground soil (sieved through a 2-mm sieve). The VisNIR spectra of soil samples were used to predict total petroleum hydrocarbon (TPH) content in the soil using partial least squares (PLS) regression and boosted regression tree (BRT) models. Each model was validated with 30% of the samples that were randomly selected and not used in the calibration model. The field-moist intact scan proved best for predicting TPH content with a validation r2 of 0.64 and relative percent difference (RPD) of 1.70. Because VisNIR DRS was promising for rapidly predicting soil petroleum hydrocarbon content, future research is warranted to evaluate the methodology for identifying petroleum contaminated soils.

Cover crops are the plants which are grown to improve soil fertility, prevent soil erosion, enrichment and protection of soil, and enhance nutrient and water availability, and quality of soil. Cover crops provide several benefits to soils used for agriculture production. Cover crops are helpful in increasing and sustaining microbial biodiversity in soils. We summarized the effect of several cover crops in soil properties such as soil moisture content, soil microbial activities, soil carbon sequestration, nitrate leaching, soil water, and soil health. Selection of cover crops usually depends on the primary benefits which are provided by cover crops. Other factors may also include weather conditions, time of sowing, either legume or non-legume and timing and method of killing of a cover crop. In recent times, cover crops are also used for mitigating climate change, suppressing weeds in crops and increasing exchangeable nutrients such as Mg 2+ and K +. Cover crops are also found to be economical in long-term experiment studies. Although some limitations always come with several benefits. Cover crops have some problems including the method of killing, host for pathogens, regeneration, and not immediate benefits of using them. Despite the few limitations, cover crops improve the overall health of the soil and provide a sustainable environment for the main crops.

Reasons for the gradual genetic yield improvement (10–30 kg ha−1yr−1) reported for soybean [Glycine max (L.) Merr.] during decades of cultivar development are not clearly understood. Identification of mechanisms for the yield improvement would aid in providing indirect selection criteria for streamlining cultivar development. Our objective was to identify yield components responsible for yield improvement in 18 public southern cultivars released between 1953 and 1999. The study was done at the Ben Hur Research Farm near Baton Rouge, LA (30°N Lat) during 2007 and 2008, plus a validation study in 2009. Experimental design was a randomized complete block with four replications and one factor (cultivar). In the 2007–2008 study, 18 cultivars released across the 1953–1999 period were selected. Three old and three new cultivars were used for the 2009 validation study. Data were obtained on yield, seed m−2, seed size, seed per pod, pod m−2, pod per reproductive node (a reproductive node is one having at least one pod having at least one seed), reproductive node m−2, percent reproductive nodes and node m−2 Data were analyzed by ANOVAR and mean separation. Regression and path analyses were also done between yield and yield components, year of release and yield components, and among yield components themselves. Results of the 2007–2008 study indicated that yield differences were sequentially controlled by node m−2, reproductive node m−2, pod m−2, and seed m−2 However, node m−2 was not as accurate at distinguishing low and high‐yielding cultivars as the other three yield components and its role in yield formation was not substantiated in the validation study. A possible indirect selection criterion for yield during cultivar development is reproductive node m−2

Understanding how defoliation affects soybean [Glycine max (L.) Merr.] yield during the seed‐filling period will aid in making management recommendations for control of stresses that reduce yield through defoliation. Because previous research has studied defoliation effects at only one or two specific stages of seed filling, our objective was to gain a greater understanding of the mechanisms for yield reduction with defoliation at weekly intervals across the seed‐filling period. Defoliations were conducted from the bottom of the canopy upward to mimic the progress of soybean rust. Two experiments, one in Kentucky (38° N lat) and the other in Louisiana (30° N lat), were conducted in randomized complete block designs in split‐split plot arrangements with four replications. Main plots were two cultivars, split plots were defoliation timings at weekly intervals during seed filling, and split‐split plots were defoliation levels of 0, 33, 66, and 100% leaf removal. Data were obtained on yield and several growth dynamic and yield component factors. Defoliation‐induced yield losses corresponded more closely with percentage of light interception reductions than percentage of leaf area reductions. During the R5 to R6 period, defoliation levels sufficient to reduce light interception by 18 to 23% were required to cause yield loss. Yield losses from total defoliation were greatest at early seed filling (78%) but gradually diminished as seed filling progressed. Each 0.1 delay in developmental stage resulted in a 5% decline in yield loss.

Because of increased seed costs for soybean [Glycine max (L.) Merr.], minimizing plant population to a level that still optimizes yield (minimal optimal plant population) has become important. Because little is known about possible genotypic strategies addressing this problem, the objectives of this study were to (i) determine the relative accuracy of three putative selection criteria for genotypic differences in minimal optimal plant population and (ii) use the best criterion to identify cultivar differences within the southern U.S. public cultivar germplasm collection. Two studies to address these objectives were conducted near Baton Rouge, LA (30° N lat). The first study (in 2007 to 2008) involved eight cultivars grown at normal (198,000 plants ha−1) and sparse plant populations (10,000 plants ha−1). Cultivar potential for a low minimal optimal plant population was assessed by relative yield (%) in sparse vs. normal plant population. Normalized branch dry matter (BDM) per plant (BDM per days to R5) was the most accurate selection criterion for minimal optimal plant population. This criterion was then used in a second study to assess minimal optimal plant population differences among 41 cultivars. Wide differences in normalized BDM per plant occurred, ranging from 0.10 to 1.50 g per plant d−1. In conclusion, normalized BDM per plant is an accurate and efficient selection measure for minimal optimal plant population.

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Predicting yield loss from defoliating agents such as insects, diseases, and hail is an important objective for commercial soybean [Glycine max (L.) Merr.] production. Because of its association with canopy photosynthetic rate, light interception has potential use as a yield‐loss prediction tool for defoliation‐induced yield loss. Our objective was to review data from previous defoliation research to generate regression models relating relative yield [(yielddefol / yieldcontrol) × 100] to reduced relative light interception (LI) [(1 − LIdefol / LIcontrol) × 100]. Regression models were developed for the flowering/pod formation period, and for the early, mid, and late seed filling periods. Highly correlated (R2 = 0.74 to 0.95) linear and quadratic regression models were generated relating relative yield to relative light interception reduction. Validation of these models was done using independent studies from Iowa, Alabama, and Louisiana. Observed and predicted relative yields were highly linearly correlated (R2 = 0.93) in a 1:1 linear relationship having a zero y‐intercept, thus demonstrating that our models were robust yield‐loss prediction tools for defoliation‐induced yield loss. Use of these tools as economic thresholds for insecticide application was also discussed and compared with other economic threshold methods (insect counts and percent defoliation).