Numerous studies have been conducted to comprehend the economic importance of market participation for producers. However, a significant gap remains, particularly in Central Asia. This study assesses market participation drivers for Kyrgyzstan’s milk producers using the Life in Kyrgyzstan dataset, encompassing a comprehensive nationwide farm-level survey. Findings reveal that household assets boost market entry and sales volume, whereas distance from markets discourages participation but can increase sales for active sellers, reflecting transportation cost strategies. The insights inform policy interventions benefiting rural small-scale milk producers in Central Asian countries that share similar cultural backgrounds.

Consumer preferences are often influenced by reference-dependent preferences. This study investigates the influence of reference-dependent preferences on the estimation of willingness to pay (WTP) for table grape attributes elicited by a second-price auction. We evaluate two models: the attribute-based reference dependence model, where individuals compare the target product’s attributes with their favorite ones, and the alternative-based reference dependence model, where comparisons are made with a reference product. Results show that including reference points impacts the WTP estimation for different attributes, with varying levels of loss aversion, suggesting the attribute-specific influence of reference points.

Historical ambiguity on how cover crop use influences future crop insurance eligibility has been proposed as one explanation for low cover crop adoption rates. However, explicit guidance on cover crop use for crop insurance participants was added in the 2018 Farm Bill. This study uses farm level data from the Agricultural Resource Management Survey to ascertain whether crop insurance participation influenced adoption of cover crops and to what degree that influence persisted after the 2018 Farm Bill. Estimation of a double hurdle model, combined with a control function approach to address endogeneity, suggests statistically and economically significant effects between crop insurance expenditures and cover crop use at the “extensive margin,” but no statistically significant effect at the “intensive margin.” Estimation on subsets of the data defined by before and after the 2018 Farm Bill suggest that the effect is primarily attributable to participation trends prior to the 2018 Farm Bill. Following the 2018 Farm Bill, no statistically significant effects are observed between cover crop use and crop insurance expenditures.

The study uses the Living Standards Measurement Study–Integrated Surveys on Agriculture to evaluate mobile phone ownership’s direct and indirect effect on yields in Tanzania. The results indicate that transitioning from not owning to owning a mobile phone improves maize yields by about 16%. Mobile phones indirectly affect maize yield by facilitating farmers’ access to extension services – regardless of the type of provider – but only account for about 2% of the total effects. Considering both direct and indirect effects, this study suggests that extension services partially moderate this causal relationship. Further, the impact of mobile phones is stronger among male-headed farm households.

India is a leading producer as well as one of the largest exporters of rice. Export competitiveness of rice thus plays a crucial role in India’s overall trade scenario, particularly in bridging the gaps in country’s soaring trade deficits. The study investigates the impact of key determinants on India’s rice export competitiveness for the period 1990–2020 using the autoregressive distributed lag model. The results reveal that increasing yield of rice and depreciated exchange rate have significant positive impact on India’s rice export competitiveness. In the long run, production cost and export price have a detrimental effect on export of rice from India. However, India’s trade openness and export price do have a mixed effect on export competitiveness of rice in the short run. Moreover, the findings of this study offer some important policy implications for the stakeholders like farmers, exporters, and the government to improve export competitiveness of Agri-products including rice.

This work shows that direct combustion of cotton gin waste (CGW) at cotton gins can profitably generate electricity. Many bioenergy processing centres emphasise very large-scale operations, which require a large and stable bio-stock supply that is not always available. Similarly, a small biorefinery processing gin trash at a cotton gin must wrestle with the high volatility of cotton yields and price variation in cotton and electricity. Fortunately, the smaller scale allows these risks to be somewhat countervailing. Low cotton yields allow the limited gin trash available to be applied to the highest peak electricity prices in winter. Similarly, high yields with low cotton prices generate revenue from power generation throughout high winter electric prices.

To assess the profitability of an onsite power plant requires high-resolution data. We utilise hourly electricity price data from 2010 to 2021 in West Texas and obtain a small data array of 15 years of gin trash at a medium-sized gin. Prior analyses have had neither. We leverage limited CGW data to better leverage generous electricity price data by generating a Bayesian distribution for CGW. We simulate 10,000 annual CGW outcomes and electricity prices. Using engineering parameters for combustion efficiency, we show the expected internal rates of return of 19–22% for a 1 MWe and a 2 MWe plant at a small gin. Simulations then compare economic returns to the variance of those returns, which allows the analyst to present to investors a frontier of stochastic dominant return outcomes (risk-returns trade-off) for plants of different sizes at different sized gins.

This study introduces a data-driven benchmarking method to assess the relative price and profit efficiencies of cattle auction sales. Transaction inefficiencies represent the divergence between observed sales and their corresponding maximum feasible value. Data envelopment analysis is used to assess market efficiencies and to identify the optimal array of animal characteristics based on peer comparisons. In Texas, about half of the evaluated transactions were inefficient. Furthermore, by modifying some of the observed attributes, sales prices could increase by 23% and profits improve by 74%. Efficiency results can be used to develop effective value-added management practices and educational programs.

The decline in fed cattle cash sales and its impact on price discovery are concerning. This study extends existing literature by utilizing machine learning to explore factors, particularly decision trees and random forests, to explore factors influencing fed cattle price ranges, complementing traditional regression analyses. These models uncover hidden patterns and provide additional insights into the cattle market. Key variables such as weight range, head count, and trade location, are found to be associated with price ranges. Notably, the weight range emerges as the primary variable influencing the price range, with smaller weight ranges linked to lower price ranges.

Little is known about the preferences of US at-home gardeners for potting mix characteristics. This study uses a Best-Worst Scaling approach to evaluate consumer preferences for eleven characteristics of potting mix. The most important characteristics identified are formulated for specific plant or garden types, pre-mixed ingredients, and price. The least important are the brand, packaging, and home delivery. There is some variation in the relative importance of these potting mix characteristics depending on consumer demographics. This study guides Industry stakeholders and policymakers on product development while enhancing environmental sustainability.

We examine the convergence of lean hog futures and cash prices, focusing on the thinning of negotiated cash markets. Using daily Livestock Mandatory Reporting data from 2001 to 2024, we confirm significant non-convergence between negotiated and futures prices over the past two decades. Regression results show that as the share of negotiated transactions declines, the absolute basis increases, emphasizing the critical role of negotiated markets in ensuring convergence. These findings highlight concerns about the reliability of negotiated prices as a benchmark for contracts and offer valuable insights for price risk management in the hog industry.

Oklahoma has witnessed a remarkable expansion of its medical marijuana industry since legalization in 2018, emerging as the largest in the nation in terms of both dispensaries and growers per capita. However, the ramifications of this burgeoning sector remain largely unexplored in rural areas of the state. A focus group in one rural community provides information on the most important perceived impacts of the industry, which include influences on local housing values. An event study confirms that high-grower (but not high-dispensary) counties saw housing value increases of roughly 20% post-legalization when compared to neighboring states where marijuana remains illegal.

The 2018/2019 trade conflict between the United States and China impacted a broad array of agricultural products, including soybeans. Previous trade studies using gravity models fail to account for trends and complex seasonal patterns observed in the data. This study uses a machine learning (ML) approach to estimate losses in soybean export value and volume from the trade war. We find that models using ML techniques outperform traditional models and estimate losses in the value of soybean exports of $10.16 billion/year. The ML models fit the complex export trade data series well, highlighting the importance of utilizing improved modeling approaches.

This study uses established procedures to estimate the effects of changes in mortality and growth implant protocols on feedlot net returns (NRs). We then propose new methods for estimating concurrent impacts to feedlot greenhouse gas emissions intensity. Reducing mortality consistently increases NRs and reduces greenhouse gas emissions intensity in the feedlot regardless of sex or placement weight. Results indicate that use of two implants in the feedlot may increase NRs and reduce greenhouse gas emissions per pound of dressed beef produced, compared to just one growth implant.

We analyze 2017–2021 data to examine whether weekly options offer unique insights compared to regular options on grain futures. These weeklies, increasingly popular for short-term use around major United States Department of Agriculture (USDA) reports, are found to be more effective in predicting near-term volatility, challenging the conventional view of longer-dated options’ superiority. However, they overprice realized volatility by 420 basis points (bps) for corn and 280 bps for soybeans. Major USDA reports add a premium of 650 bps for corn and 240 bps for soybeans, highlighting a trend toward more time-sensitive trading strategies, though the long-term impact on market efficiency of weeklies remains subdued.

Relationships between broiler cut prices are evolving as production and consumer preferences change. Stronger price relationships are observed within white and dark meats categories than between them. This can be attributed to the unique substitutability of cuts within categories, while weaker relationships between categories arising from their roles, white meats are predominantly consumed domestically, while dark meats are more oriented towards export. The analysis also shows that own price is the main driver of price variation, suggesting that treating chicken as a single category may be misleading. Considering different cuts may offer valuable insights into economic/market analysis for other meats.

Novel plant-based meat alternatives (PBMAs) have the potential to disrupt traditional meat industries, but only if consumers substitute PBMAs for meat over time. This study uses weekly household scanner data from 2018 to 2020, to estimate demand for PBMAs in the ground meat market. We use a basket-based demand approach by estimating a multivariate logit model (MVL) to determine cross-product relationships between PBMAs, ground turkey, ground chicken, and ground beef, while simultaneously exploring the role of prior consumption habits and demographics on demand. The only demographic characteristic affecting PBMA demand is the household education level of having a college degree when controlling for other factors. We found no significant seasonal difference in purchasing patterns, after controlling for cross-product effects, prior purchases, and demographics. Demand for PBMAs is driven by habit formation rather than variety seeking, as higher past purchases of PBMAs lead to a higher likelihood of current PBMA purchases. Consumers with higher past ground beef purchases are less likely to choose PBMAs, suggesting growth of this new product is coming from consumers on the margin rather than from heavy beef buyers substituting away from their traditional purchases. PBMAs and ground beef are utility complements with all meat products, suggesting that traditional meat and PBMA companies, along with retailers, should explore synergies in product marketing and offerings.

The USDA has implemented policies to address inequities for socially disadvantaged farmers and ranchers. This research examines agricultural risk inequities and the impact of 2018 Farm Bill programs on crop insurance use among minority and veteran farmers. Results indicate that minority and veteran farmers are disproportionately located in regions of the U.S. with higher risks of drought and excess precipitation. Yet, these producer groups had lower use of crop insurance prior to the implementation of the 2018 Farm Bill. However, the incentive programs created under the 2018 Farm Bill have increased use of federal crop insurance among these vulnerable populations.

Increasing consumer demand for sustainably-sourced products has created a need to benchmark sustainability at the field level. To address this issue, some companies are offering incentives to producers, but are still lacking participation. This study estimated producers’ willingness to accept for participating in sustainability programs and implementing sustainable practices at the field level using a double-bounded dichotomous-choice framework. The results revealed preferences for longer contracts in length of time, industry as the verification party, supplemental benefits that yield an economic incentive, and a per-bale payment. This project will give new insights to the value and importance of documenting, verification, and traceability throughout the supply chain.

This study analyzes empirically and graphically the effects of the relative market price level on the premiums and discounts received associated with alfalfa hay quality. Findings indicate that at times when alfalfa prices are elevated relative to the average, there is no significant impact on the average premiums and discounts received. Conversely, at times when alfalfa prices are depressed relative to the average, there are statistically significant variations in the premiums and discounts across quality grades compared with a market characterized by average prices. Understanding these dynamics can aid producers in optimizing production decisions and assist consumers in managing input costs.