Cotton warehouses face unique inventory management challenges. This research addresses this challenge by proposing innovative strategies to enhance warehouse efficiency. Three key objectives are addressed: modeling cotton bale movements, evaluating an alternative to the current bale handling process, and evaluating an alternative to the current cotton bale marketing system. Results reveal significant cost savings. Changing bale receiving and placement strategy by using gin codes yields a $499,000 per-cycle reduction for an Oklahoma cotton warehouse case. Altering order fulfillment techniques, such as grouping 30 orders, saves $34,000 per cycle. Implementing quality-based bale substitution leads to a $1.3 million saving per cycle.

Anthropogenic nutrient loading from land use, especially agriculture, is a major threat to waterbodies worldwide. Efforts to govern nutrient pollution are increasingly based on simulation modeling for research, evaluation, and regulation. This study develops a novel approach to simulate nutrient losses from agriculture applied to the Lake Champlain basin in the US state of Vermont. The Vermont Phosphorus-Index—a farm-based empirical model regularly used for site evaluation—is scaled up to the basin level with high-resolution geographic data and probabilistic estimation of unknown parameters and management practices. Results are comparable with analyses using more data and computationally intensive tools. Important insights into basin-wide management include: (1) nutrient-management planning can significantly reduce P losses in a livestock-agriculture-dominated watershed by re-distributing manure applications from areas of high loss to low loss; (2) hotspot identification from geographic data alone may be deeply complicated by high underlying heterogeneity of soil phosphorus; and (3) probabilistic modeling using simple, field-scale models is a potentially useful complement to complex watershed process models. Findings suggest that currently available best-management practices will likely be insufficient to reach reduction targets in the most impaired sub-watersheds. Reductions of agricultural land use and herd size, particularly in intensive dairy operations, may be necessary.

As of 2019, there were 940 hate groups known to be active in the United States. Previous examinations of hate groups have assumed formation. This paper uses simulation modeling to test Weber’s 1947 socio-political theory of charismatic leadership. Simulation modeling creates a computer simulation that simplifies people and their interactions to mimic a real-world event or phenomena. Three models of hate group formation were created to test this theory. These experiments test the importance of personal and societal levels of hate in group formation and the influence of charismatic leadership. These experiments also tested hypotheses regarding the number of groups that form, the speed of formation, and group size. Data were collected from 13,000 model iterations to test these hypotheses. All models successfully generated hate groups. Hate groups formed at all levels of societal hate. An in-depth understanding of how hate groups form may assist in slowing the proliferation of these groups and decreasing their appeal.

Poor productivity in smallholder farming systems has necessitated research on the potential of crop–livestock integration to sustainably improve productivity. The study hypothesized that improvement in individual agronomic and livestock systems and synergistic utilization of by-products of either system increases productivity, profitability and integration. Smallholder farming households were classified into: old and resource endowed (OR); part time (PT); and young, risk-taking and enthusiastic (YRE) following a survey conducted in Murehwa and Goromonzi districts of Zimbabwe. Crop–livestock systems’ integration scenarios were developed for each farmer category. Expression of crop–livestock integration in physical terms, e.g., kg ha−1, can be complex and confounding, hence the expression of integration in monetary values. Baseline scenario results indicate that OR had the highest crop–livestock integration of $3981 compared with PT and YRE despite OR having the lowest manure usage compared with PT and YRE farmers. Moreover, OR had the least legume yields of <800 compared with 3530 kg ha−1 in YRE farmers. Subsequent crop–livestock integration scenarios increased maize grain yields by at least 50%, thus increasing profitability to $1210, $3230 and $3100 yr−1 for mucuna, cowpea and groundnut, respectively. Total income increased by 135, 132 and 101% translating to $9880, $2960 and $6290 yr−1 in OR, PT and YRE farmers, respectively. Crop–livestock integration therefore has the potential to improve smallholder crop and livestock productivity, variable with socio-economic status.

Velvetleaf from Mississippi and Wisconsin and soybean (var. Williams) were grown in five temperature regimes (12/4, 19/11, 26/18, 33/25, and 40/32 C day/night) in controlled-environment chambers. Leaf appearance rates increased with temperature in both species, ranging from 0.06 to 0.69 leaves per day in velvetleaf and 0.07 to 0.38 leaves per day in soybean. The threshold temperature for leaf appearance in both species was 5 to 6 C. The largest leaves of both species were produced at 26/18 C. By 55 d after emergence, the greatest total leaf area per plant occurred at 26/18 C or above in both species. Reproductive development occurred earliest at 33/25 C in velvetleaf and at 26/18 C in soybean. This limited vegetative growth in velvetleaf more than in soybean. The weed/crop ratio for total leaf area increased with increasing temperature, indicating that velvetleaf probably would be more competitive with soybean under higher temperatures. The two populations of velvetleaf generally responded similarly to temperatures.

Glyphosate-resistant (GR) weeds have been a prime challenge to the sustainability of GR cotton-based production systems of the midsouthern United States. Barnyardgrass is known to be a high-risk species for evolving herbicide resistance, and a simulation model was developed for understanding the likelihood of glyphosate resistance evolution in this species in cotton-based systems. Under a worst-case scenario of five glyphosate applications in monoculture GR cotton, the model predicts resistance evolution in about 9 yr of continuous glyphosate use, with about 47% risk by year 15. A unique insight from this model is that management in response to GR Palmer amaranth in this system (a reactive response) provided a proactive means to greatly reduce the risks of glyphosate resistance evolution in barnyardgrass. Subsequent model analysis revealed that the risk of resistance is high in fields characterized by high barnyardgrass seedbank levels, seedling emergence, and seed production per square meter, whereas the risk is low in fields with high levels of postdispersal seed loss and annual seedbank loss. The initial frequency of resistance alleles was a high determinant of resistance evolution (e.g., 47% risk at year 15 at an initial frequency of 5e−8 vs. 4% risk at 5e−10). Monte Carlo simulations were performed to understand the influence of various glyphosate use patterns and production practices in reducing the rate and risk of glyphosate resistance evolution in barnyardgrass. Early planting and interrow cultivation are useful tools. Crop rotation is effective, but the diversity of weed management options practiced in the rotational crop is more important. Diversifying weed management options is the key, yet application timing and the choice of management option is critical. Model analyses illustrate the relative effectiveness of a number of diversified glyphosate use strategies in preventing resistance evolution and preserving the long-term utility of glyphosate in midsouthern U.S. cotton-based production systems.

Protected area managers often face uncertainty when managing invasive plants at the landscape scale. Crested wheatgrass, a popular forage crop in the Great Plains since the 1930s, is an aggressive invader of native grassland and a problem for land managers in protected areas where seeded roadsides and abandoned fields encroach into the native mixed-grass prairie. Given limited resources, land managers need to determine the best strategy for reducing the cover of crested wheatgrass. However, there is a high degree of uncertainty associated with the dynamics of crested wheatgrass spread and control. To compare alternative management strategies for crested wheatgrass in the face of uncertainty, we conducted a decision analysis based on information from Grasslands National Park. Our analysis involves the use of a spatially explicit model that incorporates alternative management strategies and hypotheses about crested wheatgrass spread and control dynamics. Using a decision tree and assigning probabilities to our alternative hypotheses, we calculated the expected outcome of each management alternative and ranked these alternatives. Because the probabilities assigned to alternative hypotheses are also uncertain, we conducted a sensitivity analysis of the full probability space. Our results show that under current funding levels it is always best to prioritize the early detection and control of new infestations. Monitoring the effectiveness of control is paramount to long-term success, emphasising the need for adaptive approaches to invasive plant management. This type of decision analysis approach could be applied to other invasive plants where there is a need to find management strategies that are robust to uncertainty in the current understanding of how these plants are best managed.

Annual bluegrass is one of the most diverse plant species in the world and is the most problematic winter annual weed in commercial turfgrass. Continuous application of the same herbicide mechanism of action for annual bluegrass control on golf courses has increased herbicide-resistant populations. The purpose of this research was to simulate six herbicide-use strategies to evaluate the risk of annual bluegrass resistance evolution to glyphosate. In a worst-case scenario of yearly glyphosate applications at dormancy, resistance evolved within 10 yr and was predicted to evolve in > 90% of populations by yr 20. When glyphosate was rotated on alternate years with a unique mechanism of action, resistance was delayed for 12 to 15 yr. Season-long control of annual bluegrass often requires multiple herbicide applications. Therefore, additional strategies were simulated in which glyphosate was applied at dormancy with combinations of PRE and/or POST herbicides at various timings. Resistance was most effectively delayed with a PRE application in late summer, a POST application in fall, and alternating glyphosate with a different POST option at dormancy. This delayed resistance by 25 yr and a 35% risk was predicted after 50 yr. Strategies utilizing three annual herbicide applications with unique mechanisms of action were more effective for controlling population growth compared to other strategies. Resistance was predicted to evolve within 35 yr for each of the strategies simulated. However, these results indicate annual bluegrass herbicide resistance can be managed by using an integrated herbicide program, rotating unique mechanisms of action as frequently as possible.

Herbicide-resistant barnyardgrass has become widespread in the rice production systems of the midsouthern United States, leaving few effective herbicide options for controlling this weed. The acetolactate synthase (ALS)- and acetyl-CoA carboxylase (ACCase)-inhibiting herbicides remain largely effective in Clearfield® rice production, but strategies need to be developed to protect the long-term utility of these options. A two-trait model was developed to understand simultaneous evolution of resistance in barnyardgrass to the ALS- and ACCase-inhibiting herbicides in Clearfield rice. The model was used to predict resistance under a number of common weed management scenarios across 1,000 hypothetical rice fields in the Mississippi Delta region and answer some key management questions. Under an ALS inhibitor–only program consisting of three annual applications of imidazolinone herbicides (imazethapyr or imazamox) in continuous Clearfield rice, resistance was predicted within 4 yr with 80% risk by year 30. Weed management programs that consisted of ALS- and ACCase-inhibiting herbicides such as fenoxaprop and cyhalofop greatly reduced the risk of ALS-inhibiting herbicide resistance (12% risk by year 30), but there was a considerable risk for ACCase resistance (evolving by year 14 with 13% risk by year 30) and multiple resistance (evolving by year 16 with 11% risk by year 30) to both of these mechanisms of action. A unique insight was that failure to stop using a herbicide soon after resistance evolution can accelerate resistance to the subsequent herbicide option. Further, a strong emphasis on minimizing seedbank size is vital for any successful weed management strategy. Results also demonstrated that diversifying management options is not just adequate, but diversity combined with timely herbicide applications aimed at achieving high efficacy levels possible is imperative.