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.

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.

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.

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.

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.

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.

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.

Bovine trichomoniasis is a venereal disease that causes significant losses in the US beef industry. The USDA Animal and Plant Health Inspection Service views bovine trichomoniasis as endemic and delegates control to state agencies and producers. Disease management’s positive externalities are not reflected in a producer’s profit maximization problem, leading to potentially suboptimal levels of control. Our objective was to assess the economic impacts of 50% and 100% reductions of herd-level bovine trichomoniasis prevalence. The cumulative present value of net welfare increased by $388.856 and $193.222 million under the 100% and 50% scenarios, respectively. Feeder cattle producers and retail beef consumers benefit most from enhanced control.

This study explores several alternative specifications of futures-based forecasting models to improve existing approaches constrained by restrictive assumptions and limited information sets. In lieu of historical averages, our approaches use rolling regressions and include current market information reflected in the deviation of the current basis from its historical average. To mitigate potential challenges arising from nonstationarity and structural changes in the relationship between farm and futures prices, we employ a 5-year rolling estimation window. We find that a rolling regression approach offers significant improvements (as evidenced by our Modified Diebold–Mariano test) in the accuracy and information content of forecasts of cotton season-average prices (SAPs) mostly at short forecast horizons.

To sustain its agricultural output, the U.S. relies heavily on imports of potash. Using two different methodologies, this study finds that U.S. import demand falls by approximately four percent in the short run and nine percent in the long run for a 10% rise in the potash import price. Price transmission with leading exporter Canada is imperfect, with U.S. prices rising only 7.7% in the short run when Canadian prices rise by 10%. The findings suggest that policies such as import tariffs would raise costs for U.S. agricultural producers because there are few potash substitutes.

In this paper, we examine the effect of the “Fresh From Florida” marketing program on the preferences of consumers located in geographically distant regions. We administered a choice experiment to consumers from the Northeastern region of the US, the Eastern region of Canada, and from Florida. Our findings show that the logo recall rate is significant for out-of-state consumers. While the WTP for the “Fresh from Florida” attribute is not statistically significant for Northeastern US respondents, logo recall positively influences the WTP. Logo recall positively affects WTP in Canada, but only for respondents with positive or neutral opinions of Florida.

The study analyzes productive efficiency of crop farming in the EU. We use publicly available data on crop farming from FADN database. Standard efficiency measurement techniques based on frontier analysis indicate that the representative farms provided in the database are fully efficient, even though there is ample evidence in the literature that this is highly unlikely. We find that this is a consequence of overly restrictive assumptions about the compound error in standard SF models. The efficiency benchmark, based on the best model given data with generalized error specification, reveals substantial differences in crop farming efficiency in the EU.

Here we examine product attributes present in dry dog food to show there exist potential price premiums and discounts associated with health and wellness attributes in dry dog food. The findings indicate price premiums are associated with attributes related to digestion and allergy care. Pricing discounts are found to be associated with immune support and dental attributes. The results of this study are anticipated to be a starting point for more sophisticated and dynamic analysis of pricing and willingness-to-pay studies in the pet food industry.

Past research shows that farm animal welfare (FAW) policies can reduce consumer and retailer welfare, but producer welfare implications are less certain. This study uses equilibrium displacement modeling of the U.S. wholesale shell egg market to determine how the transition to cage-free egg sales could affect short- and long-run producer welfare. Under varying assumptions and retailer demand shifts, the results consistently demonstrate that producer profits are expected to decline as retailers pivot toward cage-free purchasing, holding all else constant. These findings help explain the tension surrounding FAW policies across the supply chain and can be used to inform industry and policymaker discussions on the topic.

Constrained econometric techniques hamper investigations of disease prevalence and income risks in the shrimp industry. We employ an econometric model and machine learning (ML) to reduce model restrictions and improve understanding of the influence of diseases and climate on income and disease risks. An interview of 534 farmers with the models enables the discernment of factors influencing shrimp income and disease risks. ML complemented the Just-Pope production model, and the partial dependency plots show nonlinear relationships between income, disease prevalence, and risk factors. Econometric and ML models generated complementary information to understand income and disease prevalence risk factors.