Agricultural Value Research Articles

Sodium hypochlorite–based binderless technology for solid wood adhesion: application of a commercial bleaching agent for adhesion and observation of surface characteristics

AbstractIn this study, several methods for a novel binderless technology were explored, the effects of treatment conditions on the adhesive performance were examined and the surface was characterized during adhesion. The method entailed a three-step process for bonding solid wood, which involved immersion in sodium hypochlorite–containing treatment solutions, surface scratching, and drying with pressing. Various treatment conditions were investigated, including solution types, immersion durations, scratch treatment materials, and pressing methods. The resulting shear strength was 9.7 MPa, which is well above the Japanese Agricultural Standards value for laminated wood (6 MPa). In this method, the specimens were immersed in a bleach solution containing a surfactant for 16 h, followed by pressurization at 1 MPa for 24 h at 60 ℃ using a pressure-controlled press. Around the bonding area in the specimens, the cells in the immersed area were significantly compressed, and the aldehyde groups of the coniferyl aldehyde in the lignin were denatured or lost. Based on these findings, a new binderless technology was developed for solid wood without a high-temperature, high-pressure pressing step.

Forecasting Grain Production in Hubei Province Based on Markov Modeling

Abstract. Grain production plays a crucial role in economic and social development. Accurate prediction of grain production can help agricultural decision-makers allocate resources rationally and do a good job at the next stage of the arrangements for the deployment of the work.This article used the production data, which was from 2023 Hubei Provincial Statistical Yearbook,to simulate and predict the grain production for several years,with the method of Markov prediction. It can be seen from the results that the average fitting error of the model is 3.47%. Using the text dataset of grain production from 2020 to 2022, the results show that the model can effectively improve the prediction accuracy of agricultural grain procuction. Finally, the model is used to predict the agricultural output value structure of China from 2023 to 2027. Markov prediction can improve the predictability and initiative of the work and provide scientific basis for the future decision-making of agricultural managers, and has guiding significance for the work of grain storage and distribution.

Promoting Agripreneurship as a Means of Employment in Ogun State

The integration of entrepreneurship into Nigeria's agricultural sector presents a promising avenue for addressing the country's persistent youth unemployment and food insecurity challenges. This study explores agripreneurship as a viable means of employment in Ogun State, focusing on various agripreneurship options, their perceived viability, and their potential to foster economic sustainability. A survey of 1,200 farm owners and workers randomly selected across diverse agriculture value chain in the three senatorial district of Ogun State was conducted. Data obtained were analysed with descriptive statistics and the hypothesis were tested with logistics regression analysis of the specified logit model. The results revealed that production-related activities, particularly poultry, livestock farming, and fishery, are perceived as the most viable agripreneurship options. However, input supply and support services require further development to enhance their attractiveness and viability. The findings underscore the need for targeted interventions to promote agripreneurship, particularly in underdeveloped areas, to maximize its potential for job creation and economic growth. However, challenges such as access to land, capital, and technology persist, hindering the full adoption of agripreneurship. The study concludes that promoting agripreneurship through targeted interventions could create sustainable employment opportunities for Nigerian youths, enhance agricultural productivity, and contribute to broader economic growth. The results also highlight the need for further development in areas like input supply and support services to make them more attractive to potential agripreneurs.

Genetic and phytochemical evaluation of M2 generation mutants of fenugreek (Trigonella foenum-graecum L.) induced by gamma rays and Ethyl Methane Sulphonate (EMS).

Fenugreek (Trigonella foenum-graecum L.) is highly esteemed for its therapeutic properties and is widely used in traditional medicine and modern pharmacology. Enhancing its genetic traits and phytochemical profile, particularly its trigonelline content, can significantly increase its medicinal and agricultural value. This study aims to investigate the effects of gamma rays and Ethyl Methane Sulphonate (EMS) as mutagenic agents on the genetic and phytochemical characteristics of the M2 generation of fenugreek, focusing on genetic diversity and desirable trait enhancement. To achieve this, various concentrations of EMS and gamma rays were administered to fenugreek seeds, and 27 traits were assessed in the resulting M2 generation. These traits were analyzed for variance, mean values, and correlations. The genetic diversity of 23 M2 offspring was investigated using nine Start Codon Targeted (SCoT) markers. The genetic diversity assessment involved Principal Coordinate Analysis (PCoA) and cluster analysis, utilizing the Dice similarity coefficients and the Unweighted Pair Group Method with Arithmetic Mean (UPGMA). A Bayesian model provided deeper insights into the genetic structure. Results revealed that lower doses of gamma rays (100Gy) and EMS (0.2%) positively impacted specific traits. In comparison, higher doses (200Gy and 0.4% EMS) increased seed trigonelline content to 0.71mg/g dry weight. Among the SCoT markers, SCoT-9 was the most efficient, segregating the populations into three clusters. The first three principal components in the PCoA explained 20% of the total variance, leading to seven subgroup populations distinction. These findings underscore the potential of induced mutagenesis in enhancing desirable traits in fenugreek.

Assessing the impact of agriculture value chains on food and water environments in Meerut District, India

AbstractWater and food are interconnected elements crucial for agricultural production and food security. While sufficient water resources have facilitated significant food production, intensive agricultural practices have contributed to the degradation of water resources. Agriculture value chains play a pivotal role in shaping food systems and can lead to virtual water movement through the production and trade of water-intensive crops. These chains affect both water and food environments, with water being a driver and food determining factors like availability and accessibility. Despite the economic benefits, agriculture value chains pose non-economic challenges, including environmental and social impacts. The focus on profitability often overlooks sustainability and equity concerns, leading to mono cropping and exclusion of small farmers from markets. This trend is evident in Meerut district, situated in the fertile Indo Gangetic plain and a top farming state in India. Despite its agricultural potential, the district has shifted towards sugarcane monoculture, impacting water and food environments. This study aims to explore the complex relationship between agriculture value chains, water sustainability, food availability and accessibility. The objectives include understanding these interlinkages, analyzing baseline scenarios, and assessing the implications of value chains on food and water environments. The analysis reveals the adverse effects of sugarcane monoculture on water environment, food environment and agricultural sustainability. To address these challenges, the study puts a way forward to adopt value chains approach for the strategical promotion of crop diversification, emphasizing resilience and sustainability.

Artificial Intelligence Tools for the Agriculture Value Chain: Status and Prospects

Artificial Intelligence Tools for the Agriculture Value Chain: Status and Prospects

Texas Rural Land Market Integration: A Causal Analysis using Machine Learning Applications

Texas rural land markets have several special features that makes it unique from other rural land markets in the United States. In 2021, Texas agricultural land, including buildings, is valued at $299.88 billion, which is almost 10% of the nation's total agricultural real estate value and 83% of the state's land is categorized as rural. In addition, due to its size and geologic features, Texas’ diverse landscape creates complex and widely divergent conditions affecting ownership and marketing of the land. Despite this complexity, lack of granular level and reliable transactional data on land sales has prevented thorough investigation into Texas land markets to uncover various interdependencies. Using quarterly transactional land value data from 1966-2017, this study uses cutting-edge machine learning algorithms and probabilistic graphical models to uncover causal interaction patterns of different land markets in Texas. The results reveal that Texas rural land markets are interdependent. Current and potential landholders and lenders can use the results from this work to aid strategic decision making. Financial institutions and investment groups could be made aware of the trend of one land market relative to other markets and adjust their holdings accordingly. Landowners may better understand changes in net wealth, which affect their ability to borrow capital and operate efficiently. Moreover, lenders may also benefit from the information to manage collateral and thus maintain the stability of their operation.

Detecting the Role of Agriculture and Industry Value Added, Energy, Capital Flow, and Openness to Trade upon Environmental Kuznets Curve for India

Detecting the Role of Agriculture and Industry Value Added, Energy, Capital Flow, and Openness to Trade upon Environmental Kuznets Curve for India

Effects of Agricultural Insurance on Water Pollution in China

Water pollution is the most serious environmental problems in China now, water pollution directly affects people's quality of life and the sustainable development of agriculture, and the biggest source of water pollution is agricultural non-point source pollution, so we need to pay more attention to the environmental impact of agricultural production and reduce agricultural non-point source pollution. This paper examines the environmental impact of agricultural insurance on water pollution. Using annual data from 30 provinces across China between 2011 and 2019, this paper employs a two-way fixed-effects model to examine the correlation between agricultural insurance development and agricultural pollutant emissions. Additionally, it takes agricultural carbon emissions and agricultural output value as dependent variables to comprehensively investigate the environmental and economic impacts of agricultural insurance. The research results indicate that the development of agricultural insurance has a significant effect on reducing agricultural water pollution and carbon emissions. The pollutant reduction effect is more significant in the major grain-producing areas. The positive environmental effect of agricultural insurance is based on the fact that the development of agricultural insurance is conducive to reducing the use of pesticides and chemical fertilisers and does not come at the expense of increased agricultural output.

BATS: Bat‐aggregated time series—A python‐based toolkit for landscape‐level monitoring of free‐tailed bats via weather radar

Abstract The US operates a system of 160 S‐band Doppler weather radars known as NEXRAD (NEXt generation weather RADar) that continuously monitors the airspace around the majority of the United States and outlying territories. These radars detect and track birds, insects, and bats. Free‐tailed bats (Genus Tadarida) provide considerable ecosystem services through their voracious insect consumption; but their movements and ecosystem service provision have historically been difficult to track/study in space and time. We introduce ‘BATS’, a Python toolkit that streamlines the process of downloading, classifying, and aggregating time series of free‐tailed bats across large landscapes. BATS retrieves data from NOAA's weather radar data repositories and classifies the processed radar data using a pre‐trained ML trained to detect and classify radar echoes associated with free‐tailed bats. We trained various machine learning approaches at classifying pixels containing free‐tailed bats and compared the effectiveness across approaches. With an AUC of 0.963, the neural network approach is highly effective in identifying free‐tailed bats in NEXRAD data over our study sites in California and Texas. Furthermore, BATS is capable of quickly distilling 6 months of radar data from a single tower (3.5 Tb) into a single 15 Mb‐sized map of bat occurrence, contingent on available computing resources. BATS will help scientists and stakeholders identify areas of high bat occupancy at the landscape level over long periods of time. This ability has the potential to increase our understanding of the economic and agricultural value of these species.

Racial group differences and export market access challenges in agricultural value chains and supply networks of SMEs in South Africa

ABSTRACT Although previous studies provide an insightful and categorised nature of internal and external export barriers experienced by agricultural SMEs in general; there is little, if any, strategic insights on the racial nuances in value chain barriers for most agricultural SMEs that target global export markets. This study posits that export barriers are not uniform and differ based on race and the context in which the agricultural SME is based. Hence, we specifically explore the nature of export barriers per race within the agro-value chains in South Africa and juxtapose these with the dynamic market and historical conditions of the South African market environment. In this multi-case study, we focused mainly on the differences between black and white-owned agricultural SMEs riding on their different supply networks’ capabilities to navigate the export barriers they face. The findings serve to develop seminal knowledge to enhance export capabilities and opportunities for minority groups globally and more importantly to highlight the underlying drivers for racial imbalances in agro-supply chains. Although the findings are geographically skewed to South Africa, the market dynamics are relevant across countries with similar demographic characteristics and provide a platform for future research.

Preparation of Biofertilizers from Banana Peels: Their Impact on Soil and Crop Enhancement

Disposing of banana peels (BPs) as bio-organic waste is an environmental challenge. Rich in potassium, BPs are often discarded despite their agricultural value. Reports on the valorization of BP are increasing, but no review has focused on BP fertilizer preparation methods. This study aims to review and analyze these methods to guide researchers and agriculturists in optimizing BP utilization, promoting sustainable waste management, and effective agricultural practices. This review has uncovered significant findings. A composite of banana and orange peels emerged as the most favoured and widely used approach, closely followed by dried BPs. This study highlighted the substantial impact of fertilizer application methods such as top and basal dressing. Most of the results revealed that the peels significantly improved the growth parameters of various plants. However, for biochar, the plant height was insignificant between treatments, further emphasizing the importance of the application method used. Banana peels are a valuable resource for biofertilizer synthesis. The banana–orange peel composite exhibits outstanding fertilizer properties. More new studies should go beyond the seedling stage, especially to harvest. This would give more information on the performance and viability of BP fertilizers.

A Fuzzy Logic Approach to Performance Evaluation of Genomics-Driven Crop Improvement

Genomics-driven crop improvement has emerged as a revolutionary approach to enhance yield, resilience, and nutritional value in agriculture. However, traditional performance evaluation methods often must catch up with agricultural systems' complex, multifaceted nature. This study proposes a fuzzy logic-based framework to assess the performance of genomics-driven crop improvement initiatives. By integrating fuzzy logic, we can effectively manage uncertainty and ambiguity inherent in agricultural data, allowing for a more nuanced evaluation of crop traits, environmental factors, and management practices. The paper begins by elucidating the fundamental principles of genomics-based agriculture and its impact on crop development. It then delves into the theoretical underpinnings of fuzzy logic and its applicability in modeling the multifaceted aspects of crop growth, encompassing genetic, environmental, and physiological factors. Through a synthesis of existing research and empirical data, the paper illustrates the effectiveness of fuzzy logic in capturing the nuances of crop development processes, including germination, flowering, and yield formation. This paper underscores the pivotal role of fuzzy logic analysis in advancing genomics-based agriculture, offering insights into the dynamic interactions shaping crop development and facilitating informed decision-making for sustainable and resilient food production systems in the face of evolving environmental challenges. Results indicate that the fuzzy logic approach enhances the accuracy of performance assessments and facilitates better decision-making in crop management.

Artificial intelligence in food and nutrition evidence: The challenges and opportunities based on a convening of content experts

Abstract Science-informed decisions are best guided by the objective synthesis of the totality of evidence around a particular question and assessing its trustworthiness through systematic processes. However, there are major barriers and challenges that limit science-informed food and nutrition policy, practice, and guidance. First, insufficient evidence, primarily due to acquisition cost of generating high-quality data, and the complexity of the diet-disease relationship. Furthermore, the sheer number of systematic reviews needed across the entire agriculture and food value chain, and the cost and time required to conduct them, can delay the translation of science to policy. Artificial intelligence (AI) offers the opportunity: 1) to better understand the complex etiology of diet-related chronic diseases; 2) to bring more precision to our understanding of the variation among individuals in the diet-chronic disease relationship; 3) to provide new types of computed data related to the efficacy and effectiveness of nutrition/food interventions in health promotion; and 4) to automate the generation of systematic reviews that support timely decisions. These advances include the acquisition and synthesis of heterogeneous and multimodal datasets. This perspective summarizes findings from a meeting of experts convened at the National Academy of Sciences, Engineering and Medicine. The purpose of the meeting was to examine the current state and future potential of AI in generating new types of computed data as well as automating the generation of systematic reviews to support evidence-based food and nutrition policy, practice and guidance.

Interdependent Effects of Agro-input Node Interventions on the Adoption of Cattle milk Production Technologies among Smallholder Farmers in Kericho County, Kenya

Agricultural extension service delivery system is a vital component in agricultural development initiatives of any nation. Several extension approaches are used to achieve these initiatives. Globally, many Governments and development partners in rural agricultural development initiatives have gradually transitioned away from total state control of rural community and agricultural extension service delivery into adopting and supporting a more multifaceted system built on holistic participatory approach commonly referred to as Agricultural Value Chain Extension Approach (AVCEA). This AVCEA is relatively new in developing countries, including Kenya, and specifically in Kericho County where exploration on its impact on farm-level technology adoption has yet to be undertaken. The purpose of this study was to investigate the interdependent effects of agro-input node interventions on the adoption of cattle milk production technologies among smallholder farmers in Kericho, Kenya. This study adopted an ex-post-facto research design and systematic random sampling technique to select a sample of 132 farmers who previously exposed to AVCEA. Interviews were conducted for eight (8) dairy production experts and 6 Dairy organization representatives as key informants. The primary data were collected using interview schedules, questionnaires and focus group discussion tools. The data were analyzed for descriptive statistics like frequencies, percentages and inferential statistics like Chi-square tests with the aid of SPSS version 26.0. The results of the study indicated that there was a significant interdependence between some agro-input value chain node interventions and the adoption of cattle milk production technologies. There was also Interdependence between ease of access to farmers ‘groups/cooperative societies and the adoption of cattle milk production technologies by the smallholder farmers. Based on findings of this study, Agricultural experts, stakeholders, and other concerned bodies should focus on developing strategies to strengthen interventions at the agro-input node, particularly by fostering cooperation among smallholder farmers to enhance collective actions.

Response Mechanism and Evolution Trend of Carbon Effect in the Farmland Ecosystem of the Middle and Lower Reaches of the Yangtze River

The farmland system in the global terrestrial ecosystem has dual attributes as both a carbon source and a carbon sink, playing a crucial role in controlling carbon emissions and mitigating global warming. Using carbon source and sink accounting of farmland ecosystems, we applied methods such as standard deviation ellipse, Tapio decoupling theory, and Markov chain to analyze the spatiotemporal changes, response mechanisms, and evolutionary trends of regional carbon effects. The results indicated that from 2011 to 2021, the farmland ecosystem in the middle and lower reaches of the Yangtze River consistently acted as a carbon sink. However, the net carbon sink showed slight fluctuations and significant spatial differences. The migration range of the net carbon sink center in the farmland ecosystem of the middle and lower reaches of the Yangtze River was relatively small, ranging from 115.52 to 115.77° E and 30.14 to 30.27° N. The decomposition of the Tapio decoupling index between the net carbon sink of the farmland ecosystem and agricultural output value showed the order of effects on their coupling relationship as follows: agricultural mechanization level > agricultural mechanization efficiency > agricultural output value > planting scale. The probability of maintaining the original state of net carbon sink in various cities in the middle and lower reaches of the Yangtze River (over 77%) was much higher than the probability of transfer, making it difficult to achieve a leapfrog growth in net carbon sink. The net carbon sink at the city scale exhibits the Matthew effect and spatial spillover effect. The above research results clarify the spatiotemporal changes in carbon effects in agricultural production at multiple levels, including city, province, and region. They also provide a theoretical basis for formulating differentiated regional emission reduction and sink enhancement strategies in the middle and lower reaches of the Yangtze River, promoting the rapid development of low-carbon agriculture in China.

Ergos: redefining storage infrastructure and market access for small farmers in India

Learning outcomes After completion of the case study, students will be able to analyse the path of the entrepreneurship from idea generation to market development to scaling up business, examine the impact of start-ups like Ergos on India’s agriculture value chain, discuss the challenges faced by tech entrepreneurs in growing a business, identify problems solved by Grain Bank Model and evaluate digitisation of farming’s custodial services such as warehousing, market linkages and loans. Case overview/synopsis The case study discusses how founders of Ergos, India-based leading digital AgriTech start-up, Kishor Kumar Jha and Praveen Kumar, started one of the unique models in the AgriTech landscape in India. After noticing the grim condition of small and marginal farmers in Bihar, India. Kishor and Praveen decided to put their banking and corporate experience to use in the farming sector. Ergos aimed to empower farmers by providing them with a choice on when, how much quantity, and at what price they should sell their farm produce, thus maximising their income. As a result, Ergos launched the grain bank model, which provided farmers with doorstep access of end-to-end post-harvest supply chain solutions by leveraging a robust technology platform to ensure seamless service delivery. Ergos faced many challenges in its journey related to financing, marketing and distribution. Amidst these developments, it remained to be seen how Kishor and Praveen would be able to realise their goal to serve over two million farmers across India by 2025 and create a sustainable income for them through its GrainBank Platform. Complexity academic level This case study was written for use in teaching graduate and postgraduate management courses in entrepreneurship and business strategy. Supplementary materials Teaching notes are available for educators only. Subject code CSS 3: Entrepreneurship

Empowering rural South Asia: Off-grid solar PV, electricity accessibility, and sustainable agriculture

This study investigates the impact of off-grid solar photovoltaic (PV) systems on electricity accessibility and agricultural sustainability in rural South Asia, employing panel data methodology: least square dummy variable (LSDV) and Lewbel instrumental variable (IV) approach. The findings demonstrate a robust positive association between solar PV adoption and increased electricity access, even when accounting for individual and period-specific factors. Increased electricity access significantly contributes to higher agricultural value added per worker, but not necessarily to the overall share of agriculture in the economy. This analysis also reveals a positive and significant impact of solar PV (SPV) on agricultural value added per worker, independent of rural electrification levels. Heterogeneities across time and context, along with country-specific factors, play crucial roles in shaping these relationships. While factors like government effectiveness and GDP per capita have complex and sometimes counterintuitive effects, access to land, agricultural employment, and capital investments all contribute positively to agricultural sustainability. The Lewbel IV method offers strong impact of SPV on electrification, independent of external factors, and its direct contribution to agricultural output, make it a compelling tool for empowering rural communities and building a more sustainable future in South Asia. Robustness of the result is validated through three approaches: measuring variable averages at three-year sub-periods, applying three-stage least squares (3SLS), and utilizing alternative methods (panel corrected standard errors (PCSE), feasible generalized least squares (FGLS)) to consider various data characteristics. Off-grid solar PV emerges as a game-changer for rural electrification and a catalyst for sustainable agricultural development in South Asia. Governments and development agencies can prioritize investments in solar PV systems to address energy poverty and boost agricultural productivity.

Transformation of the Rural Nonfarm Economy During Rapid Urbanization and Structural Transformation in Developing Regions

This article reviews the past 25 years of empirical research on the rural nonfarm economy (RNFE) in developing countries; that literature has evolved in ways that track and mirror the rural transformation itself. Since 2000, rapid urbanization, structural transformation, and a sharp series of economic, climate, and disease shocks have influenced trajectories in the RNFE. It has grown to become, on average, the predominant source of income for rural households in developing countries. Both segments of the RNFE—activities linked to agricultural value chains as well as those unrelated to the agricultural sector—have grown. Understanding how each component of the RNFE influences rural incomes, employment, resilience, women, youth, and farming and natural resource management practices is, therefore, more important than ever.

How spatial differences in climate affect agricultural land values in New Zealand

How spatial differences in climate affect agricultural land values in New Zealand