Agricultural Policy Makers Research Articles

Credit Financing and Agricultural Development in Nigeria

This study investigates the short-run dynamics and long-run link between credit financing offered by Deposit Money Banks (DMBs) and agricultural development in Nigeria for a period of 1981 to 2020. A quantitative ex-post facto research design was employed to analyze bank credit impact on agricultural advancement at both aggregate and sub-sectoral agricultural production. The research strategy utilized allows the use of economic series obtained from Central Bank of Nigeria Statistical Bulletin (2021) and World Bank Development Indicators (2021) to be analyzed by Autoregressive Distributed Lag (ARDL) estimation technique. From ARDL estimations, it is indicated that DMBs loans and advances have significantly negative short-run impacts on agricultural development in Nigeria at both aggregate and sub-sector levels. However, Bounds Testing analysis shows that the impacts of DMBs financing on Nigeria agriculture sector and labour employment in the sector become positively significant over a long period at aggregate level and for crop production only as a sub-sector. But the observed positive long-run impact of DMBs lending to agriculture was not significant for advancement of other sub-sectors (livestock, fishing and forestry). Hence, the study affirms that in the short run DMBs lending significantly slow down agricultural development in Nigeria but increases it significantly in the long run. The study recommends that there is need for agricultural policy makers and governments in Nigeria to develop appropriate specific agricultural financing mechanism that can aid performance of agricultural sector in the short-run and maintain long-run positive gains in the sector.

Screening Optimal Oat Varieties for Cultivation in Arid Areas in China: A Comprehensive Evaluation of Agronomic Traits

This study was undertaken to identify oat (Avena sativa L.) varieties optimal for cultivation in the Jiuquan region, China, in 2021. A selection of 27 domestic and international oat varieties were analyzed, considering ten key agronomic traits, including plant height, stem diameter, spike length, leaf width, and yield. Employing methods such as cluster analysis, principal component analysis, and grey correlation degree, a comprehensive evaluation was conducted. The principal component analysis distilled the ten indicators to three core components. The most influential factors in the first principal component were plant height, ear length, and hay yield, while leaf length and leaf area index were the highest contributors to the second component. The stem-to-leaf ratio emerged as the principal indicator in the third component. The cluster analysis resulted in the classification of the 27 oat varieties into 3 categories. Following a comprehensive evaluation through the grey correlation degree and principal component analysis methodologies, we found that the oat varieties Sweety 1, Fuyan 1, Dingyan 2, Baler, Quebec, and Longyan 2 received the highest scores. These varieties, hence, appear to be the most suitable for cultivation and promotion in the Jiuquan region. This study thus provides invaluable insights into oat cultivation practices, offering guidance for farmers, agricultural policymakers, and future research in the field.

Improving the crop classification performance by unlabeled remote sensing data

Accurate and timely crop classification results play a crucial role in providing data support for agricultural policy-making and crop yield estimation. However, the current development of crop classification faces a bottleneck in improving classification performance due to limited labeled samples and saturated classification algorithms. In this study, we propose a novel method to improve crop classification performance by leveraging unlabeled remote sensing data (URSD). Importantly, our method does not necessitate a large number of labeled samples or significant modifications to the classification algorithm. Instead, it relies on a unique self-supervised training approach and a substantial amount of URSD. Specifically, we develop a self-supervised classification framework based on a Multilayer Perceptron (MLP) and introduce a self-supervised training approach that takes into account both temporal and spectral factors. Additionally, we construct a historical sample classification model based on crop growth knowledge, emphasizing the correlation of local time series. We evaluate the proposed method using four study areas in China. The analysis of pre-training data types reveals that our method not only improves the classification performance of current year samples but also demonstrates noticeable improvement in classifying historical samples. The classification method analysis demonstrates the ability of our proposed self-supervised learning training approach to accumulate more prior knowledge. Overall, these results highlight the advantages of our method in terms of classification efficiency and performance improvement.

Market opportunities seizing capability and fish farming firm performance: A dynamic managerial capability perspective

The role that owner-managers’ managerial capabilities play in agricultural firms' strategic change and performance is still unclear. A firm's market opportunities seizing capability (OSC) is a source of competitiveness and superior performance. The objective of this study is to establish how owner-manager's dynamic managerial capabilities (DMCs) including managerial human capital, managerial social capital and managerial cognition affect firm performance through the mediating effect of OSC, as well as the moderating effect of market dynamism in the relationship OSC and firm performance. 306 cross-sectional data collected at fish farming firms (FFF) level in southern Benin were used and partial least squares structural equation model was applied to test research hypotheses. The results of SmartPLS4 reveal that owner-managers’ DMCs including business ties (BTs), political ties (PTs), and managerial cognition (MC) demonstrate a significant effect at improving FFF market OSC and performance. Finding also underline the significant contribution of market dynamism to FFF performance as well the mediating role of market OSC in the relationship between BTs, PTs, MC, and FFF performance. Findings suggest that FFF owner-managers can achieve superior performance by strengthening their DMCs while improving market OSC. The study provides useful insights to FFF owner-managers, agriculture policymakers and practitioners who are engaged in agriculture firms' strategic management and sustainable performance.

Current practices, challenges and new advances in the collection and use of food composition data for Africa

High-quality food composition data are indispensable for improved decision-making in food security, health policy formulation, food labeling, diet formulation, agricultural policymaking, nutrition research, and many other nutrition-related activities. The optimisation of dietary patterns is a powerful tool to reduce the impact of malnutrition on a population’s health and well-being. Many countries in resource-poor settings lack a framework for developing and managing food composition data appropriate for these purposes. In the article, an overview of available food composition tables in Africa and the origin, use and limitations of theses tables are discussed. It is important that those working on any nutrition-related activity for resource-poor settings understand the limitations of current food composition data. Production of high-quality data requires the harmonization and adoption of international standards and guidelines across Africa. Moreover, continuity in the production, compilation and management of high-quality food composition data is challenged by suboptimal capacity building in terms of organizational, institutional and legal framework development. In this perspective article, the authors deliberate on challenges with a focus on Africa, while discussing new advances in food composition activities. Opportunities (such as the Internet of Things (IoT), wearable devices, natural language processing (NLP) and other machine learning techniques) to improve existing resources must be more actively explored and supported.

The impact of CAP subsidies on the productivity of cereal farms in six European countries: A historical perspective (2008–2018)

Productivity is an always-hotly-debated issue within the EU agricultural policymaking, particularly concerning the effect of subsidies distributed via the Common Agricultural Policy (CAP). The cereal sector is under particular political scrutiny nowadays, with the Ukraine conflict and environmental issues menacing food security levels for both developing and developed countries.Using individual farm data from 2008 to 2018 for France, Germany, Italy, Poland, Spain, and the United Kingdom, we investigate the effects of different subsidies on the cereal farms’ productivity, accounting for the intrinsic farms’ productivity levels (low, medium and high productive farms). The System-GMM estimator, combined with Inverse Mills Ratios, is applied within a three-step strategy to overcome endogeneity problems. Results highlight how CAP subsidies affect farms’ Total Factor Productivity (TFP), and to what extent they differ according to the nature of the subsidy, the country, and, interestingly, the intrinsic farm’s productivity level. The analysis reveals that TFP embeds an autoregressive behaviour that should always be accounted for when assessing the relationship with subsidies. Most of the CAP subsidies entail a negative-to-unsignificant effect on TFP, except for agri-environmental subsidies, which can increase farms’ productivity. Interestingly, the negative role of direct payments seems more recurrent in the low productive farms, accelerating their exit from the market.

Mathematical model and optimal control analysis of coffee berry borer with temperature and rainfall variability

This study focuses on a nonlinear deterministic mathematical model for coffee berry borer (Hypothenemus hampei) with temperature and rainfall variability. In the model analysis, CBB free and coexistence equilibria are computed. The basic reproduction numbers at a minimum and maximum temperature and rainfall are derived. The qualitative analysis of the model revealed the scenario for equilibria together with basic reproduction numbers. The local stability of equilibria is established through the Jacobian matrix and the Routh–Hurwitz criteria, while the global stability of equilibria is demonstrated using an appropriate Lyapunov function. The normalized sensitivity analysis has also been performed to observe the impact of different parameters on basic reproduction numbers. The proposed model is extended into an optimal control problem by incorporating two control variables, namely, the preventive measure variable based on the separation of susceptible coffee berries from contacting the pests based on biological control and an increase in the death rate of colonizing females of CBB based on chemical control. Optimal disease control analysis is examined using Pontryagin’s minimum principle. Finally, the numerical simulations are performed based on analytical results and are discussed quantitatively. Furthermore, the cost-effectiveness of control strategies to determine the best approach to minimize the CBB burden was studied. The study is significant in providing reliable information on how one can use mathematical modeling to improve the roles of control strategies and prevention in CBB transmission in a coffee farm. The outcome of the study may guide public agriculture policymakers on optimal control strategies to control the pests. In particular, using chemical pesticides is very effective to combat pests with minimum costs.

Perspectives on stakeholder participation in the design of economic experiments for agricultural policymaking: Pros, cons, and twelve recommendations for researchers

AbstractEconomic experiments have emerged as a powerful tool for agricultural policy evaluations. In this perspective, we argue that involving stakeholders in the design of economic experiments is critical to satisfy mandates for evidence‐based policies and encourage policymakers' usage of experimental results. To identify advantages and disadvantages of involving stakeholders when designing experiments, we synthesize observations from six experiments in Europe and North America. In these experiments, the primary advantage was the ability to learn within realistic decision environments and thus make relevant policy recommendations. Disadvantages include complicated implementation and constraints on treatment design. We compile 12 recommendations for researchers.

Steering ‘green’ innovation policy toward sustainability? Lessons from implementing EIP-AGRI in Sweden

This article critically examines the state's steering capacity of ‘green innovation’ programs using Sweden's implementation of the agricultural European Innovation Partnership (EIP-AGRI) as a case representing part of the EU's Green Deal. The innovations should promote the competitiveness of rural areas and contribute to national environmental protection and climate goals.We found that despite expectations, implementation rests on compartmentalized networking within the agricultural sector, prioritizing increased ‘competitiveness’ before ‘green’ development, and interpreting ‘innovation’ mainly in the technical sense. The results indicate that the state´s steering capacity of ‘green innovation’ programs meets several obstacles: the overall goals from the top tend to be both too many and too vague, leaving it to the administration to interpret what kinds of features should be prioritized from below. The state's steering in the case of EIP-AGRI relies mostly on internal agricultural expertise contrary to previous research that suggests a recent ‘de-compartmentalisation’ of European agricultural policymaking.

Factors Influencing the Spatial Distribution of Regulating Agro-Ecosystem Services in Agriculture Soils: A Case Study of Slovakia

Agro-ecosystem services assessment and mapping are one of the main requirements for implementing the concept of ecosystem services into institutional decision-making within the European Union and worldwide. The aim of this study was to identify the most important regional drivers of the natural potential of regulating ecosystem services with agricultural soil in Slovakia, resulting in an original set of macro-scale spatial stratification criteria for agricultural land, and to estimate typical middle values of its potential in newly defined regions. The non-monetary assessment was based on natural environment parameters and land use factors. For the assessment of individual indicators of agro-ecosystem services, we utilized an expert scoring system. We evaluated ecosystem services on the basis of biophysical indicators that determine the corresponding soil functions and are part of the existing databases available in the Slovak Republic. The new methodological combination enabled us to provide unique mapping and assessment of agro-ecosystem services within Slovakia. Regional climate, land cover, and soil slope were identified as key factors impacting agro-ecosystem services potential within the country, which can be used as data stratification levels for further analyses. Linking the value of individual regulating agro-ecosystem services potential with geographical distribution can help to optimize its potential depending on the needs of the inhabitants living in different regions by introducing appropriate measures and can contribute to effective agricultural policymaking.

RESEARCHING TRENDS AND FORECASTING FUTURE VALUES OF FRUIT EXPORTS AND IMPORTS OF THE REPUBLIC OF SERBIA

Considering that the Republic of Serbia has a surplus in the foreign trade of fruits, the problem of this research refers to the perception of the importance of the foreign trade of fruit of the Republic of Serbia. The subject of the research includes the analysis of previous and future trends in the value of exports and imports of fruits, as one of the most important agro-industrial products. The aim of the research is to determine future trends, as well as to forecast future values of fruit imports and exports using econometric methodology in the field of analysis of variations of time series, i.e. statistical methods of linear trend. The contribution of this research study is multiple, in scientific terms it enriches the existing scientific literature, given that research studies that have addressed this issue are very rare, while in practical terms it provides guidance to producers and exporters of fruits and agricultural policy makers, especially in terms of encouraging fruit exports from the Republic of Serbia in the coming period.

Effect of farm power on farm household livelihood in Makurdi local government area of Benue state

This study investigates on the effect of farm power on household livelihood in Makurdi Local Government Area of Benue state. A random and purposive sampling were used to select farmers from different council wards and a total 100 respondents were selected as sample size for the study. Factor analysis, and multiple regression were used to determine the factors affecting the use of farm power, examine the effect of tractor power on area cultivated of food crops, analyze the effect of tractor power on farm productivity and assess the effect of tractor power on poverty level of farmers in the study area. The study revealed that an increase in tractor power used will lead to increase in area cultivated of food crops by 2.24 hectares; a unit increase in tractor power use will lead to increase in farm productivity by 361.05 tonnes per ha; a unit increase in tractor power used will lead to decrease in poverty level of farmers by 0.77 units. The study therefore recommends that, Agricultural policy makers should place more emphasis on the development of small-scale farmers particularly in the use of appropriate and affordable farm power options, credit acquisition amongst others.

High resolution annual irrigation water use maps in China based-on input variables selection and convolutional neural networks

Water use and available water resources are the prerequisites for optimal water resources allocation, which is an efficient way to alleviate water scarcity all over the world. However, the spatial scales of water use and available water resources are often not matched due to their different ways for data surveying or observing. The spatial distributed (grid-scale) water use map is one of the ways to convert the spatial scales of the water use and available water resources. As irrigation is essential to guarantee sustainable development of agriculture, food security and economy, and accounts for most water use in China. A framework to generate high resolution (1 km) annual irrigation water use maps has been proposed. An iterative input variables selection (IIS) algorithm is adopted to select the optimal input variables among candidate input variables. After determining the optimal input variables, the convolutional neural network (CNN) is built to establish the relationship between the selected input variables and the irrigation water use. The spatial distributed irrigation water use maps can be produced by the trained CNN with the selected input variables at grid scale. The spatial distributed annual irrigation water use maps in China is produced at 1 km resolution from 1998 to 2013, and the performance of the framework has been proven to be credible with three metrics (i.e., root mean squared error, Nash Sutcliffe efficiency coefficient and relative error). Based on the spatial distributed annual irrigation water use maps, the spatial autocorrelation of annual irrigation water use in China is weak and the clustered pattern is random with 99% confidence. The temporal changing pattern mainly depends on precipitation and soil moisture in most prefectures, and the change of the cultivated area can also lead to dramatic change of the annual irrigation water use in China. The proposed framework can generate high resolution spatial distributed annual irrigation water use maps at grid scale, and the maps can be applied to ensure the optimal water resources allocation to realize efficient utilization of water resources and the agricultural policy-making.

Cross-region plastic greenhouse segmentation and counting using the style transfer and dual-task networks

Plastic Greenhouse (PG) is a vital component of protected agriculture, that can enhance crop yields and quality by altering the local microclimate conditions. Obtaining the number and distribution of PG based on high-resolution remote sensing images is important for agricultural policymaking. However, due to the variability and interconnection of PGs in high-resolution images, counting and mapping them accurately is challenging. In addition, variation in data distribution across regions restricts the reuse of the trained model beyond the study area, impeding the cross-regional application from the source region to the target region. To address this limitation, this paper proposes a novel Cross-Regional Segmentation and Counting framework (CRSC) that integrates the unsupervised Style Transfer Network (STNet) and dual task-based Segmentation Counting Network (SCNet) to perform simultaneous PG segmentation and counting across different regions. The STNet is adopted as a target-adaptive data augmentation strategy to generate fake training samples from the source training samples. The SCNet is designed as a dual-task structure to perform the PG segmentation and PG counts simultaneously. Specifically, for each target region, we use the STNet to generate the fake training samples and then train a SCNet based on both source training samples and fake training samples. Subsequently, we leverage the trained SCNet to predict the segmentation and counting results of PGs, yielding the PG map and counting for each target region. The validation results on five target regions indicate that our proposed CRSC framework can achieve stable improvement in cross-regional segmentation and counting of PGs, especially in cases where the labeled samples in the source region are limited.

Climate change, host plant availability, and irrigation shape future region-specific distributions of the Sitobion grain aphid complex.

Understanding where species occur using species distribution models has become fundamental to ecology. Although much attention has been paid to invasive species, questions about climate change related range shifts of widespread insect pests remain unanswered. Here, we incorporated bioclimatic factors and host plant availability into CLIMEX models to predict distributions under future climate scenarios of major cereal pests of the Sitobion grain aphid complex (Sitobion avenae, S. miscanthi, and S. akebiae). Additionally, we incorporated the application of irrigation in our models to explore the relevance of a frequently used management practice that may interact with effects of climate change of the pest distributions. Our models predicted that the area potentially at high risk of outbreaks of the Sitobion grain aphid complex would increase from 41.3% to 53.3% of the global land mass. This expansion was underlined by regional shifts in both directions: expansion of risk areas in North America, Europe, most of Asia, and Oceania, and contraction of risk areas in South America, Africa, and Australia. In addition, we found that host plant availability limited the potential distribution of pests, while the application of irrigation expanded it. Our study provides insights into potential risk areas of insect pests and how climate, host plant availability, and irrigation affect the occurrence of the Sitobion grain aphid complex. Our results thereby support agricultural policy makers, farmers, and other stakeholders in their development and application of management practices aimed at maximizing crop yields and minimizing economic losses. © 2023 Society of Chemical Industry.

When my neighbors matter: Spillover effects in the adoption of large‐scale pesticide‐free wheat production

AbstractWe investigate the spillover effects in farmers’ adoption decisions of a novel pesticide‐free wheat production system. To this end, we exploit the variability and asymmetry in the social ties among neighboring farmers. We find evidence of spillover effects in farmers’ adoption decisions as well as in farm and farmer characteristics. Our results further highlight the importance of accounting for potentially heterogeneous social ties in farmer networks beyond pure measures of spatial proximity: spillover effects are only robust once we account for the strength of social ties through farmers’ stated tendency to consult peers on agricultural decisions. Our findings highlight the relevance of peer influence in the diffusion of sustainable agriculture practices even in contexts of well‐functioned institutions and high interest in environmental protection such as European agriculture. We discuss implications for the design of policies and programs for sustainable agriculture, which are currently in the center of attention in agricultural policymaking.

Information System of Superior Plant Varieties

The Food Crops Research and Development Center and research institutes are tasked with producing high-yielding plant varieties through research within the Agricultural Research and Development Agency. The results of the technology research conducted will be promoted and disseminated to the user community which includes agricultural extension workers, academics, researchers, agribusiness entrepreneurs, policy makers, and other parties who need the information. The most effective way of distributing this technological information is by creating a website-based system so that it can be easily updated and distributed.

The effect of organizational culture on employees’ performance in research institutes

Goal: This paper presents a case study, which examined the effect of organizational culture (clan culture, adhocracy culture, market culture, and hierarchy culture) on employees’ performance in a developing country context taking the case of Ethiopian Institute of Agricultural Research (EIAR). Design / Methodology / Approach: Using survey questionnaire and convenience sampling techniques, 302 complete questionnaires were returned after being filled by respondents (employees) and considered for descriptive and inferential analysis. Based on their willingness to participate in the study, four research centers within Ethiopian Institute of Agricultural Research (EIAR) were included in the study. Results: This study found out that all organizational culture types (clan culture, adhocracy culture, market culture, and hierarchy culture) have statistically significant results and have positive relationship with employees’ performance. And, of all, the regression analysis result showed that the clan culture is the most dominant culture type. Limitations of the investigation: The findings of the study depended on a single case study, and this limitation could be taken as an avenue for future researchers to examine other research institutes in similar contexts. Practical implications: The presented result sheds light regarding the effect of organizational culture on employees’ performance in agricultural research institutes; the research’s outcome provides a useful input for agricultural research institutes and policy makers in the study setting concerning the effect of organizational culture on employees’ performance. Originality/Value: this study adds to the current body of knowledge in the area of organizational culture and its effect on employees’ performance. Besides, not much earlier studies have been done on the subject of the study in the context of agricultural institutes in the developing world.

Dairy farmers' risk attitudes and their perceptions towards environmental risks in Northwest Turkey

This study aims to determine the factors that affect dairy farmers' risk attitudes and their perceptions towards environmental risks in Northwest Turkey. By the proportional sampling method, data were collected from 381 dairy farmers. The survey was performed from September 2020 to June 2021. The study data were evaluated by descriptive statistics, risk matrix method, equally likely certainty equivalent method and probit model. In this study, severe storm, excessive rainfall/flood, livestock diseases, high temperature and drought was posed an important risk for dairy farmers. The majority of them showed risk aversion behaviour. Age, education level, household size, dairy farming experience, the number of dairy cattle, the land ownership status, livestock insurance and access of agricultural credits had a significant effect on risk perceptions and attitudes of dairy farmers. As a result, these findings are expected to ensure contribution to rural development, province and region economy, insurance institutions, agricultural policy makers, and further emphasizing the importance of environmental risks in dairy farming activity

ВИКЛИКИ ТА ПЕРСПЕКТИВИ УКРАЇНСЬКОГО СІЛЬСЬКОГОСПОДАРСЬКОГО СЕКТОРУ: ПОШУК ШЛЯХІВ ЕКСПОРТУ ТА СПІВПРАЦЯ З ЄС

The article "Challenges and Prospects of the Ukrainian Agricultural Sector: Searching for Export Opportunities and Cooperation with the EU" is dedicated to the study of current challenges faced by the Ukrainian agricultural sector, as well as to the consideration of prospects and ways of development, in particular in the context of exports and cooperation with the European Union. The article offers an analysis of the problems affecting Ukraine's agricultural sector, such as limited access to foreign markets, low competitiveness and instability in the domestic market environment. The authors emphasise the need to find ways to develop a agricultural exports, in particular through strengthening cooperation with the EU. The article discusses the potential benefits of cooperation with the European Union, in particular, access to its markets and technologies, as well as opportunities to attract investment. The key areas of cooperation are outlined, such as the implementation of European quality standards, support in the field of technical infrastructure and professional development. The article also discusses the development of infrastructure, including transport and logistics, to ensure efficient exports of agricultural products. The article identifies the prospects for further development of the Ukrainian agricultural sector, in particular through cooperation with the EU. Analysing the challenges and opportunities, the authors emphasise the need to improve competitive advantages, expand export potential and improve product quality. The article concludes that successful overcoming the challenges of the Ukrainian agricultural sector and achieving sustainable development depends on active cooperation with the EU, the use of European standards and technologies, and the establishment of an effective system of support and financing for agricultural enterprises. By analysing important aspects and offering concrete recommendations, the paper is a valuable source of information for agricultural professionals, policy makers and researchers interested in the development of the Ukrainian agricultural sector and prospects for cooperation with the EU.