Search results

This study examines the heterogeneous relationship between ad-hoc support policies, high government payments, low interest rates and farm debt use across farms of different sizes and across farm operators of different races, genders and experiences to inform the 2024 Farm Bill discussions.

Utilizing USDA’s Agricultural Resource Management Survey data for 2020 and 2021, this study characterizes the differences in short-term farm debt use and the amount of short-term debt during the COVID-19 pandemic period across several farm and farmer types using double selection LASSO and regression analysis.

Results show positive associations between government payments and debt use for all farm types and farmer demographics except for residence farms and non-white farmers, which may be due to their limited access to credit. Findings also indicate that farms that could already access credit, like commercial farms, increased their short-term debt during the pandemic per the decrease in interest rates. Moreover, the 2018 Farm Bill extended certain commodity support and direct and guaranteed loan program participation provisions that were previously more closely restricted. Beginning farmers seemed more likely to use short-term debt in response to higher pandemic government payments than their more experienced counterparts.

The insights from this study are timely and useful for policymakers for designing and implementing programs related to the new 2024 Farm Bill.

One of the explanations for the results is that beginning farmers have been more likely to use debt than most other groups of operators, signaling the success of special credit provisions. Our results are relevant to making upcoming policies related to female and nonwhite farm and ranch operators.

In recent years, socially disadvantaged farmers and ranchers have increased their usage of nontraditional lending nearly converging to levels of usage observed for nonsocially disadvantaged groups. The purpose of this research is to explore explanations for this trend in lending utilization by socially disadvantaged farmers and ranchers by examining factors that influence credit usage and credit choice.

A multinomial logit is used to estimate the probability of loan choice given characteristics of the producer and farm.

While not a causal analysis, the results suggest that farm characteristics, which differ between socially disadvantaged and nonsocially disadvantaged producers, are associated with a lower likelihood of credit usage by an average socially disadvantaged farmer. For those that have loans, socially disadvantaged producers exhibit higher debt-to-asset ratios and lower current ratios, characteristics that are typically associated with higher than observed probability of usage of loans other than nontraditional. Socially disadvantaged producers also have lower value of assets which is associated with a higher probability of nontraditional loan usage.

This research is among the first to examine loan usage of socially disadvantaged producers using nationally representative data.

The purpose of this study is to examine how US farm sector performed in 2020, the first year of the pandemic. There were significant supply and demand shocks due to the pandemic. Furthermore, there was significant fluctuation in commodity prices and record high government payments in 2020. This study aims to examine the performance and position of US farm sector (financially) to system (and global economy) wide shocks.

The authors examine 2020 values for farm sector financial ratios before and after the onset of the Coronavirus (COVID-19) pandemic using the data from the United States Department of Agriculture to understand the financial position and performance of the US farm sector.

The authors find solvency ratios (which are indicators of the sector's ability to repay financial liabilities via the sale of assets) worsened in 2020 relative to pre-pandemic expectations. Efficiency ratios (which evaluate the conversion of assets into production and revenue) and liquidity ratios (which are indicators of the availability of cash to cover debt payments) showed mixed outcomes for the realized results in 2020 relative to the pre-pandemic forecasts. Four profitability ratios were stronger in 2020 relative to pre-pandemic expectations. All solvency, liquidity and profitability ratios plus 2 out of 5 efficiency ratios for 2020 were weaker than their respective average ratios obtained from 2000 to 2019 data.

This research is one of the first papers to use financial ratios to examine how the US farm sector performed in 2020 compared to expectations prior to the pandemic.

This paper examines credit products, operational performance and business models employed by nontraditional lenders (NTLs) in agricultural credit markets.

Two research methods were employed in this study: (1) an executive interview to collect primary data and (2) a case study approach to analyze the findings and develop insights.

The findings indicate the presence of significant differences among lenders across and within three categories of NTLs (large volume, vendor financing and collateral-based NTLs). For example, collateral-based NTLs employ different strategies focusing on types of loans, funding sources, commodities they support and geographic coverage to further segment the market. NTLs in this study were able to capture market by successfully identifying gaps in the supply side of agricultural credit and developing products that meet the needs of that niche (e.g. heavy renters, large operations, producers seeking fixed interest rates for term loans, financially fragile producers). Most of the interviewed NTLs had credit standards comparable to those of traditional lenders and consider them both competitors and partners since many NTLs partner with traditional lenders on participation loans, loan servicing and/or sourcing funds.

The supply side of a nontraditional lending has not been studied extensively due to the proprietary nature of data. The executive interviews conducted in this study allowed for accumulation of industry data, which is not available otherwise.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

The research identifies differences in characteristics and business strategies employed by the various categories of nontraditional lenders.

The briefing saves busy executives, strategists, and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format

Industries have been the most significant contributor to carbon emissions since the beginning of the Industrial Revolution. The transition to Industry 5.0 (I5.0) marks a pivotal moment in the industrial revolution, which aims to reconcile productivity with environmental responsibility. As concerns about the decline of environmental quality increase and the demand for sustainable industrial methods intensifies, experts recognize the shift toward the I5.0 transition as a crucial turning point.

This review study explores the convergence of green technological advancements with the evolving landscape of I5.0, thereby presenting a roadmap toward carbon neutrality. Through an extensive analysis of literature spanning from 2012 to 2024, sourced from the Scopus database, the research study unravels the transformative potential of green technological innovations, artificial intelligence, green supply chain management and the metaverse.

The findings underscore the urgent imperative of integrating green technologies into the fabric of I5.0, highlighting the opportunities and challenges inherent in this endeavor. Furthermore, the study provides insights tailored for policymakers, regulators, researchers and environmental stakeholders, fostering informed decision-making toward a carbon-neutral future.

This review serves as a call to action, urging collective efforts to harness innovation for the betterment of industry and the environment.

The apex planning body of India, NITI Aayog launched an Aspirational District Programme (ADP) in January 2018. The programme aimed to the quick and effective transformation of 112 (14%) districts of the country. This programme is considered as world's biggest result-based governance initiative having reached up to 250 million people. It is based on a ranking that is done on monthly basis. This ranking is based on 49 KPIs across six broad socio-economic themes.

The study attempts to inquire and assess the progress made by 112 Aspirational Districts under Financial Inclusion, Skill Development and Basic Infrastructure theme from the inception of the programme to June 2022 (i.e. 54 months). Instead of ranking districts with delta rank or composite scores, the study divorce from NITI Aayog's methodology of monthly delta ranking. The study explores 8 indicators under the basic infrastructure theme and 16 indicators under the financial inclusion and skill development themes. For this purpose, the study explores the availability of individual household latrines, drinking water, electricity and road connectivity. Districts are also tracked for the number of Internet-connected Gram Panchayats, and panchayats with Common Services. Every district is provided with the target as per national development priority, the study makes an effort to grasp the distance of each district from the national target. This allows researchers to develop a scale Very Far, Far, Near, Very Near, Achieved with descriptive statistics techniques. Juxtaposing the scale with timelines results in a pattern of progress made by these 112 districts.

Plant-based meat analogues (PBMAs) hold significant promise as a sustainable solution to meet future protein demands, replicating the taste and nutritional value of meat. However, the present reliance on extrusion technology in PBMA production limits the exploration of more accessible and affordable methods. The current investigation aims to meet the market demand for a scalable and cost-effective processing approach by exploring saturated steam-assisted technology that could broaden the production volume of PBMAs, thereby supplementing protein security and planet sustainability.

A one-factor-at-a-time (OFAT) approach is employed to evaluate the effect of ingredients and process conditions on the governing quality attributes (texture, colour and sensory).

Among the ingredients, monosodium glutamate (MSG) and nutritional yeast (NY) significantly enhanced the hardness and chewiness of saturated steam-assisted plant-based meat analogues (ssPBMAs) followed by potato protein isolate (PPI), defatted soy flour (DSF) and salt. The addition of PPI and DSF led to a decrease in lightness (L* value) and an increase in the browning index (BI). Sensory evaluations revealed that higher concentrations of DSF imparted a noticeable beany flavour (>20%), whereas PPI (30%) improved the overall sensory appeal. Increased levels of NY (10%) and MSG (5%) enhanced the umami flavour, enhancing consumer preference. Higher thermal exposure time (TTi) (45 min) and temperature (TTe) (120 °C) during processing resulted in softer products with reduced L* values. These findings establish a foundation for selecting and optimizing the ingredients and processing parameters in ssPBMA production.

The novelty of the current study includes process behaviour of selected ingredients such as PPI, NY, MSG, DSF, salt and adopted process conditions, namely, dough processing time (DPT), protein network development time (PNDT), TTi and TTe on the quality of ssPBMAs.

We provide a new characterization of the equality of two positive-definite matrices A and B, and we use this to propose several new computationally convenient statistical tests for the equality of two unknown positive-definite matrices. Our primary focus is on testing the information matrix equality (e.g. White, 1982, 1994). We characterize the asymptotic behavior of our new trace-determinant information matrix test statistics under the null and the alternative and investigate their finite-sample performance for a variety of models: linear regression, exponential duration, probit, and Tobit. The parametric bootstrap suggested by Horowitz (1994) delivers critical values that provide admirable level behavior, even in samples as small as n = 50. Our new tests often have better power than the parametric-bootstrap version of the traditional IMT; when they do not, they nevertheless perform respectably.

The objective of the study is to test a conceptual model based on the interrelation between data-driven supply chain quality management practices (DDSCQMP) and the performance of organized retailing firms in India.

Based on a comprehensive review of literature, the dimensions of DDSCQMP concerning the Indian organized retail sector have been extracted. Considering the research objectives, the research data has been collected using a structured questionnaire from Indian retailers. Overall 133 questionnaires were responded successfully from retailers. The model was tested using structured equation modeling (SEM) through PLS 3.0.

The research findings confirm hypotheses and reveal the statistically significant relationship between DDSCQMP and retailers' performance at an aggregate level. However, the results of the individual-level analysis of DDSCQMP appear to vary from practice to practice. Among various DDSCQMP, “customer focus” with the highest beta (ß) value was found to have the greatest impact on performance followed by “employee relations”.

The study provides empirical justification for a structural model that identifies a positive and significant relationship between DDSCQMP and organizational performance within the context of organized retail sector of India.