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Abstract Winter oilseed Brassica plants have several documented allelochemicals; however, most literature revolves around the application of seed meal to fields, or the use of aerial parts of the plant. It is not clear if these chemicals interact with the surrounding environment to the same extent if the winter oilseeds are harvested as a cash crop in the spring. To determine this, we conducted a preliminary study bioassay using water extractable compounds from canola (Brassica napus L.) and pennycress (Thlaspi arvense L.) roots diluted to 25 and 50% (v/v). These four extracts, as well as deionized water for control, were used to irrigate plates with the seeds of soybean (Glycine max L.), Palmer amaranth [Amaranthus palmeri (S.) Watson], and mare's tail (Erigeron canadensis). We measured germination percent per plate and seedling radicle and hypocotyl lengths after 8 days of treatment. Mare's tail germination was lowered by 40% on day 4 by 50% pennycress extracts, and root length was stunted by both canola and pennycress extracts by 3–5 mm. Palmer amaranth root length was stunted by 10–15 mm by both species’ extracts, as well as shoots lengthened by 5–10 mm. Extract treatments did not affect soybean germination or growth. These interactions indicate potential for allelopathic interactions in the field following harvest of dual cropped winter oilseed and herbicide‐resistant weeds and warrant further study into the topic.

Woody biomass feedstock processing, including sorting, drying, and size reduction of biomass to provide standardized reactor-ready biomass to the biorefinery, is crucial to biofuel conversion. This study compares two comminution technology systems applied to woody biomass processing at a depot before being utilized for biofuel production at a biorefinery. The conventional comminution technology, known as the hammermill system, is compared with a rotary shear system developed by Forest Concepts™. Potential economic savings of using the new technology are evaluated by applying a deterministic and a stochastic partial capital budgeting model based on results from an experiment that processed chipped hybrid poplar chips and forest residues with both systems. The stochastic partial capital model estimates that savings will vary between approximately USD 28 and USD 42 per ton of reactor-ready processed biomass, with mean and median values around USD 34 per ton. It is 90% likely that savings will be between USD 30 and USD 39 per ton of reactor-ready processed biomass. The estimated savings are mainly due to differences in input (feedstock) to output (reactor-ready biomass) yields between technologies, affecting feedstock and drying costs.

The international aviation industry has the goal to gradually reduce carbon emissions mainly by using sustainable aviation fuel (SAF). However, currently SAF cannot be produced at competitive prices relative to petroleum-based jet fuel. Pennycress is a crop whose oilseed could be used as a relatively low-cost feedstock to produce SAF, potentially benefiting farmers and the environment. This stochastic techno-economic analysis (TEA) studies an enterprise buying pennycress oilseed from farmers, extracting the bio-oil and selling it to a biorefinery that converts bio-oil into SAF. Maximum buying prices (MBP)—prices that yield a zero net present value—the crushing enterprise could pay farmers for pennycress oilseed are estimated. To conduct the analysis, discount rates are estimated based on financial data of biofuel firms, thus providing a realistic benchmark to evaluate profitability and feedstock buying prices. Estimated risk-adjusted discount rates vary between 12 and 17%, above rates typically used in similar valuations. Estimated stochastic MBP range between 10.18 and 11.73 ¢ pound−1, which is below the price at which farmers are willing to plant pennycress, according to recent research. By considering the crushing facility’s inherent cash flow structure and risk, the distributions of stochastic modified internal rate of return suggest the crushing enterprise could be economically attractive at a 14% discount rate, our most likely estimate. However, between 11 and 17% times the cash flow model is simulated, the firm falls under financial distress. Overall, the findings suggest potential barriers for deployment of a SAF supply chain without governmental incentives or related policies.

Pennycress can be used as a renewable biomass because its harvested seeds can be converted into biofuel, supplying, for example the aviation industry. Pennycress can be adopted as a winter cover crop to make extra profit in addition to summer cash crops. This study ascertains influences on row crop farmers’ interest in growing pennycress to supply a biofuels industry. The study uses data from a survey of row-crop farmers in seven US states. Effects of farm and farmer attributes on acceptance of a farmgate pennycress price are measured. Nearly 58% were interested in growing pennycress if profitable. Among those interested, 54.4% would accept the farmgate pennycress price offered. Positive influences on interest included farm size, education, and familiarity with pennycress, while concern about knowledge on growing pennycress, and use of no-till practices had negative influences. Farmers aged 40 to 65 were more likely to accept the price, while share of rented hectares and no debt had positive influences. More risk-averse farmers and those using no-till were less likely to accept. Results suggest that the majority of row crop farmers would be interested in growing pennycress if profitable, while the overall willingness to accept the farmgate price was when it was at $0.28/kg.

Farmers may be reluctant to adopt variable rate nitrogen (VRN) management because of uncertain profits. This study assessed field landscape, soil, and weather effects on optical sensing (OS)-based VRN on cotton (Gossypium hirsutum L.) N rates, yields, and net returns (NRs). Field data were collected from 21 locations in Louisiana, Mississippi, Missouri, and Tennessee, USA, between 2011 and 2014. Data included yields, N rates, and NRs for the farmer practice (FP), OS-based VRN, and OS-based VRN supplemented with other information. Production data were augmented with landscape, soils, and weather data, and ANOVA and logistic regressions were used to identify field conditions where VRN was profitable, provided risk management benefits, and improved N efficiency. Key findings indicate that NRs were improved with VRN by applying additional N on more erodible soils. Higher organic matter soils also benefited from VRN through enhanced yields and NRs. VRN may also have provided risk management benefits by providing a lower probability of NRs below NRs for the FP on soils associated with greater water-holding capacity, higher organic matter levels, or deeper profiles. Results from this study may help identify farm fields with similar characteristics for adoption of VRN management.

We determined the value of soil test information for potassium (K) in upland cotton production using the linear response plateau (LRP) and linear response stochastic plateau (LRSP) functions. A stochastic dynamic programming model was used to determine the net present value to K fertilizer when optimal K was applied with knowledge about K carryover. Using K carryover information for K application decisions increased net present value and helped maintain steady levels of soil K. The LRSP function fit the data better than the LRP, and the value of soil testing was $27 ha-1 lower over ten years using the LRSP.

The purpose of this study was to determine the effects of three particle sizes of feedstock and two types of novel bale wraps on the quality of switchgrass by monitoring the chemical changes in cellulose, hemicellulose, lignin, extractives, and ash over a 225-day period. Using NIR (Near-infrared) modeling to predict the chemical composition of the treated biomass, differences were found in cellulose, lignin, and ash content across switchgrass bales with different particle sizes. Enclosing bales in a net and film impacted the cellulose, lignin, and ash content. Cellulose, hemicellulose, lignin, extractives, and ash were different across the 225-day storage period. A quadratic response function made better prediction about cellulose, lignin, and ash response to storage, and a linear response function best described hemicellulose and extractives response to storage. This study yields valuable information regarding the quality of switchgrass at different intervals between the start and end date of storage, which is important to conversion facilities when determining optimal storage strategies to improve quality of the biomass feedstock, based on potential output yield of a bale over time.

Factors influencing adoption of variable-rate nutrient management (VRM) and georeferenced precision soil sampling (PSS) for fertilizer management among cotton producers and the factors affecting awareness of and participation in cost-share programs encouraging the adoption of nutrient-management practices were analyzed using multivariate probit regression with sample selection. Data were collected from a fourteen-state cotton producer survey. Factors including farm size, operator age, and farm location were correlated with the adoption of VRM and PSS, awareness of cost-sharing programs, and program participation. The results may help agencies target farms with the specific attributes most likely to participate in cost-share programs.

The aim of this study was to determine the impact of incorporating switchgrass samples that have been in long term storage on the development of near-infrared (NIR) multivariate calibration models and their predictive capabilities. Stored material contains more variation in their respective spectral signatures due to chemical changes in the bales with storage time. Partial least squares (PLS) regression models constructed using NIR spectra of stored switchgrass possessed an instability that interfered with the correlation between the spectral data and measured chemical composition. The models were improved using calibration sample sets of equal parts stored and fresh switchgrass to more accurately predict the chemical composition of stored switchgrass. Acceptable correlation values (rcalibration) were obtained using a calibration sample set composed of 25 stored samples and 25 samples of fresh switchgrass for cellulose (0.91), hemicellulose (0.74), total carbohydrates (0.76), lignin (0.98), extractives (0.92), and ash (0.87). Increasing the calibration sample set to 100 samples of equal parts stored to senesced material resulted in statistically increased (p = 0.05) correlations for total carbohydrates (0.89) and ash (0.96). When these models were applied to a separate validation set (equal to 10% of the calibration sample set), high correlation coefficients (r) for predicted versus measured constituent content were observed for cellulose (0.94), total carbohydrates (0.98), lignin (0.91), extractives (0.97), and ash (0.90). For optimization of processing economics, the impact of feedstock storage must be investigated for implementation in conversion processes. While NIR is a well-known high-throughput technique for characterization of senesced switchgrass, the selection of appropriate calibration samples and consequent multivariate models must be taken into careful consideration for NIR application in a biomass storage facility for rapid chemical compositional determination.

This research analyzes the adoption patterns among cotton farmers for remote sensing, yield monitors, soil testing, soil electrical conductivity, and other precision agriculture technologies using a Multiple Indicator Multiple Causation regression model. Adoption patterns are analyzed using principle component analysis to determine natural technology groupings. Identified bundles are regressed on farm structure and operator characteristics. The propensity to adopt technology bundles was greater for producers managing relatively larger operations who used a variety of information sources to learn about precision farming, irrigated cotton, practiced crop rotation, and participated in working land conservation programs.

A 2009 survey of cotton farmers in twelve states collected information about the use of georeferenced precision soil testing (PST). Adoption of PST technology and the interval until retesting were examined with a Poisson hurdle regression. Survey data were calibrated using a post-stratification weighting protocol. Farming experience, farm size, land ownership, variable rate fertilizer management plans, and the use of soil electrical conductivity devices were correlated the with period until PST adopters retested soil. Understanding how producers perceive the useful life of soil-test information may be important for monitoring the effectiveness of best nutrient management practice adoption.

This study evaluates biorefinery bio-oil feedstock costs at the plant gate for a prospective field pennycress (Thlaspi arvense L.) to sustainable aviation fuel (SAF) supply chain. The biorefinery would supply SAF to the Nashville, Tennessee international airport. Supply chain activities include pennycress production, transporting oilseed to a crushing facility, processing of oilseed into bio-oil, and transporting bio-oil to the biorefinery. The analysis shows profit potential for economic agents in the prospective supply chain. Estimated breakeven cost (profit = 0) of growing, harvesting, and transporting oilseed to a crushing facility is 17.7 ¢ kg−1. A crushing facility can pay up to 23.8 ¢ kg−1 for pennycress oilseed during the first year of production and provide investors 12.5% annual rate of return. Therefore, a profit margin of up to 6.1 ¢ kg−1 is available for the crushing facility to induce prospective pennycress producers to supply oilseed for SAF production. However, the estimated profit margin was sensitive mainly to uncertain oilseed yields, changes in field production costs, and pennycress meal and bio-oil prices. A spatial biorefineries sitting model, the Biofuels Facility Location Analysis Modeling Endeavor, estimated that the least-cost supply chain configuration is to establish three crushing facilities located in Union City, Huntington, and Clarksville, TN, to supply bio-oil to the biorefinery, with the biorefinery sited in an industrial park about 24.14 km from the Nashville international airport aviation fuel storage. Estimated total costs of bio-oil at the biorefinery plant gate are between 83 and 109 ¢ kg−1 if crushing facility oilseed procurement costs are between 17.7 and 23.8 ¢ kg−1 for oilseed.

Adoption of precision agriculture technology has arrived considerable attention, but abandonment has received little. This paper identified factors motivating adoption and abandonment of precision soils sampling in cotton. Younger producers who farmed more cotton area, owned more of their cropland, planted more non-cotton area, or used a computer were more likely to adopt precision soil sampling. Those with more cotton area or who owned livestock were more likely to abandon, while those who used precision soil sampling longer, or used variable-rate fertilizer application were less likely to abandon precision soil sampling.

Farmers are concerned about the high cost of planting herbicide-resistant cotton with the high plant densities recommended for ultra-narrow-row cotton. This study evaluated the effects on net revenues of four herbicide-resistant technology fee policies used since 1996 by Monsanto, the technology license holder. Results indicate that changes in the technology fee policy by Monsanto have raised the cost of planting herbicide-resistant cotton. As a consequence, farmers may have an incentive to switch from ultra-narrow-row cotton to wide-row cotton and to use a lower plant density when the technology fee is tied to the seeding rate.

Research has evaluated the relative profitability of variable-rate (VRT) versus uniform-rate (URT) application of a single input in fields with multiple management zones. This study addresses map-based VRT decisions for multiple inputs in fields with multiple management zones. The decision-making framework is illustrated for nitrogen and water applied to irrigated cotton in fields with three management zones. Results suggest traditional methods of determining VRT application of a single input may by suboptimal if interactions exist among VRT inputs and URT inputs. Implications are that a systems approach to multiple-input VRT decisions can produce increased net returns to VRT.

By using a stochastic frontier framework, the mutual effect of input use on production risk and inefficiency is investigated. Disentangling this mutual effect proves important for empirical reasons, at least when applied to west Tennessee cotton systems grown after various cover crops. The most striking result is that the stochastic frontier model, when compared with a typical Just-Pope model, reorders the relative riskiness of cover-crop regimes associated with the cotton systems.

In recent years, bioenergy has become a promising renewable energy source that can potentially reduce the greenhouse emissions and generate economic growth in rural areas. Gaining understanding and controlling biomass chemical composition contributes to an efficient biofuel generation. This paper presents a principal component analysis (PCA) that shows the influence and relevance of selected controllable factors over the chemical composition of switchgrass and, therefore, in the generation of biofuels. The study introduces the following factors: (1) storage days; (2) particle size; (3) wrap type; and (4) weight of the bale. Results show that all the aforementioned factors have an influence in the chemical composition. The number of days that bales have been stored was the most significant factor regarding changes in chemical components due to its effect over principal components 1 and 2 (PC1 and PC2, approximately 80% of the total variance). The storage days are followed by the particle size, the weight of the bale and the type of wrap utilized to enclose the bale. An increment in the number of days (from 75–150 days to 225 days) in storage decreases the percentage of carbohydrates by −1.03% while content of ash increases by 6.56%.

Producers in southwest Oklahoma lack adequate information about optimal planting decisions for cotton. This study uses a cotton growth simulation model to evaluate alternative cultivar, planting date, irrigation, and harvest choices. Effects of using information about soil moisture at reproduction and revenue loss at harvest in making cultivar and planting data decisions are evaluated. Using soil temperature information to plant at an early date produced high net revenue some years, but reduced mean net revenue and increased risk. Producers maximizing expected net revenue should plant a short-season cultivar in late May and use soil moisture information to schedule irrigation at reproduction.