Improve Farm Productivity Research Articles

The impact of energy structure on agricultural green productivity in China.

The agricultural development in China is currently grappling with the challenges of limited water and soil resources, environmental pollution, and ecosystem degradation. Therefore, improving agricultural green productivity has become the focus of academic attention. Based on the panel data of 30 provinces in China from 2008 to 2020, this paper constructs an index system of agricultural productivity. Subsequently, the super-efficiency SBM model is used to calculate the green productivity of agriculture, and its characteristics are analyzed from the perspectives of temporal and spatial.Through the empirical test of threshold model, it is found that China's agricultural green productivity shows a gradual growth trend from 2008 to 2020, and the growth rate has accelerated significantly in recent years. There is a non-linear relationship between energy structure and agricultural green productivity. Especially in areas with reasonable energy structure, the increase in the proportion of coal consumption will inhibit the improvement of green agricultural productivity. This paper suggests reducing dependence on fossil energy, improving market mechanism and optimizing resource utilization in order to promote green agricultural production in China. This study expands the research perspective of agricultural green productivity, provides new research methods and ideas for related fields, and provides a reasonable energy allocation plan for agricultural production, which helps to improve production efficiency and reduce costs.

Multifaceted impacts of nanoparticles on plant nutrient absorption and soil microbial communities

With the growth of the global population and the increasing scarcity of resources, the sustainability and efficiency improvement of agricultural production have become urgent needs. The rapid development of nanotechnology provides new solutions to this challenge, especially the application of nanoparticles in agriculture, which is gradually demonstrating its unique advantages and broad prospects. Nonetheless, various nanoparticles can influence plant growth in diverse manners, often through distinct mechanisms of action. Beyond their direct effects on the plant itself, they frequently alter the physicochemical properties of the soil and modulate the structure of microbial communities in the rhizosphere. This review focuses intently on the diverse methods through which nanoparticles can modulate plant growth, delving deeply into the interactions between nanoparticles and plants, as well as nanoparticles with soil and microbial communities. The aim is to offer a comprehensive reference for the utilization of functionalized nanoparticles in the agricultural sector.

Stochastic Frontier Analysis of Technical Efficiency of Smallholder Maize Farmers in Morogoro Municipality: A Reflection from RIPAT Program

This study looks into the elements that influence maize production and technical efficiency among households participating in the RIPAT SUA Project in Morogoro Municipality, using stochastic frontier analysis for the 2022 – 2023 season. The focus was on socio-economic characteristics, land size, seed usage, use of fertilizer extension services, training from the project, and education. The primary objective was to examine the factors influencing maize production and efficiency by evaluating socioeconomic aspects and specific agricultural techniques. A total of 110 households were surveyed with standardized questionnaires. Socioeconomic data was collected, key variables were evaluated and key variables were analyzed using descriptive statistics and stochastic frontier analysis. The results reveal that land size harms productivity (β = -0.436, p < 0.001), whereas improved seed usage has a positive impact (β = 0.401, p = 0.016). Education level (β = -1.168, p = 0.002) and agriculture experience (β = -0.016, p = 0.049) considerably reduce technical inefficiency, insisting on the importance of education interventions. Additionally, 40% of households have access to VSLA, revealing financial problems. While 94.55% of households got training, only 18.18% received extension services which could hamper the best productivity. Every household reported owning land and using seeds highlighting that they had the necessary materials for maize production. Regarding technical efficiency, Magadu ward high score of 5%, while Kauzeni has the lowest at 1.9% suggesting significant disproportions in productivity among wards. These findings emphasize the importance of focused initiatives to improve agriculture productivity through enhancing farmer education and training, experience, and increased access to finance. These strategies are critical for improving maize productivity and addressing food security issues among households participating in programs like the RIPAT SUA initiative hence will increase agricultural efficiency and contribute to more farmer-sustainable farming methods throughout the region

Advancing Vegetable Grafting: A Comprehensive Review of Techniques, Challenges, and the Future of Automated Solutions

Vegetable grafting, a practice that combines two plant parts rootstock and scion into a single, superior plant, is an increasingly important technique in modern agriculture. By enhancing yields, stress resistance, and disease tolerance, grafting plays a crucial role in addressing challenges such as soil borne pathogens, extreme environmental conditions, and crop vulnerability to nematodes. Despite its benefits, vegetable grafting faces several challenges, including the need for compatible plant material, labour-intensive nursery production, high seed costs, and the potential for pathogen spread. Effective grafting requires meticulous selection of rootstock and scion, precise grafting techniques, and optimal post-grafting care to ensure survival and success. Looking toward the future, significant advancements in grafting technologies and nursery practices are required to overcome these challenges and improve efficiency. The development of grafting robots, capable of mechanizing the grafting process, holds promise for increasing productivity while reducing labor costs. Additionally, the establishment of specialized plug plantlet nurseries, equipped with advanced seeders, growth chambers, and acclimatization facilities, offers a pathway to enhance seedling vigour and uniformity in developing countries. Future research should also focus on refining seed-priming methods, improving seedling storage technologies, and exploring digital tools such as databases, crop models, and mobile applications to optimize grafting practices. These innovations could provide valuable insights for growers, helping them select the best rootstock-scion combinations and manage crops more efficiently. With continued research and technological advancements, vegetable grafting is poised to become a global solution, offering sustainable growth and productivity improvements in agriculture.

Predictive Modelling of Crop Rotation Using Data Mining Approaches

Agriculture is crucial for economic growth and food security, particularly in agro-based countries. As the global population grows, the demand for food increases, necessitating improvements in agricultural productivity. Traditional methods have often fallen short, and innovative approaches such as data mining and machine learning are needed. This research aims to develop a predictive model for crop rotation using machine learning techniques. A comprehensive dataset was collected and preprocessed to train various algorithms. The proposed model demonstrated that machine learning could effectively predict suitable crops for cultivation, thereby enhancing crop yield and sustainability. The evaluation results were promising, with the Random Forest model achieving a precision of 0.67 to 1.00, recall of 0.43 to 1.00, and F1-score of 0.60 to 1.00; the Decision Tree model had a precision of 0.50 to 1.00, recall of 0.43 to 1.00, and F1-score of 0.50 to 1.00; and the K-Neighbors Classifier model showed precision of 0.40 to 1.00, recall of 0.43 to 1.00, and F1-score of 0.50 to 1.00.

Watering the Seeds of the Rural Economy: Evidence from Groundwater Irrigation in India

ABSTRACT This study explores the impact of private investment in groundwater extraction for irrigation on the spatial and sectoral distribution of rural economic activity in India. Exploiting a kink in access to groundwater, generated from an absolute technological constraint on the operational capacity of irrigation pumps with depth of the water table, there is evidence of a significant improvement in agricultural production accompanied with modest consumption gains. Groundwater extraction causes a substantial increase in population density, but has no effect on the employment rate or labor reallocation between sectors of the economy. Furthermore, irrigated agriculture appears to provide additional employment opportunities for waged labor from surrounding non-irrigated villages.

Uncovering the drivers of food insecurity in Cameroon: insights from a nationwide cross-sectional analysis

BackgroundDespite global efforts to achieve zero hunger, food insecurity remains a critical challenge in several African countries, including Cameroon. This study aims to identify sociodemographic predictors of food insecurity across Cameroon through a comprehensive, nationwide cross-sectional analysis.MethodsData for this study were drawn from the 2018 Cameroon Demographic and Health Survey (CDHS). Food insecurity levels were evaluated using the Food Insecurity Experience Scale (FIES). To estimate the probabilities of moderate and severe food insecurity among different sociodemographic groups, logistic regression models were applied, with results expressed as average marginal effects (AME).ResultsThe findings reveal that approximately 24.86% of participants experienced moderate food insecurity, while 28.96% faced severe food insecurity. Among men, the proportion experiencing severe food insecurity was 38.8%, compared to 24.3% for women. Multivariable regression analysis showed that severe food insecurity was less likely among women [AME = 0.84, 95% CI = 0.83,0.86], Muslims [AME = 0.91, 95%CI = 0.90,0.93], individuals with higher education levels (secondary education: AME = 0.93, 95%CI = 0.90,0.95; higher education: AME = 0.87, 95%CI = 0.85,0.90), those owning land (either alone or jointly) [AME = 0.92, 95%CI = 0.89,0.96], wealthiest households (moderate food insecurity: AME = 0.91, 95%CI = 0.88,0.93; severe food insecurity: AME = 0.73, 95%CI = 0.70,0.75), female-headed households [AME = 0.97, 95%CI = 0.96,0.99], and rural residents [AME = 0.97, 95%CI = 0.95,0.98].ConclusionsThese findings underscore that food insecurity affects a substantial portion of the Cameroonian population, with certain sociodemographic groups more vulnerable than others. This study proposes targeted policy recommendations to address food insecurity in Cameroon, including social assistance programs for at-risk groups, investments in socioeconomic empowerment, improvements in agricultural productivity, and ongoing research to guide evidence-based interventions.

Digitalization and Agricultural Green Total Factor Productivity: Evidence from China

Based on panel data of 31 provinces (autonomous regions and municipalities) in China from 2011 to 2022, this paper empirically examines the impact of digitalization on the inputs and outputs of the agricultural production process, and thereby derives the effects and mechanisms by which digitalization empowers the growth of agricultural green total factor productivity. The study finds that agricultural and rural areas’ digitalization significantly improves agricultural green total factor productivity, and this promotion mainly comes from the improvement of technical efficiency. Further analysis shows that digitalization mainly reduces land input and labor input, increases expected output, and reduces undesired output during the agricultural production process to achieve an improvement in agricultural green total factor productivity, indicating that digitalization has altered the allocation of agricultural factors. Heterogeneity analysis finds that the effect of digitalization on the growth of agricultural green total factor productivity is more pronounced in the eastern regions, southern regions, and areas with higher levels of agricultural digitalization, indicating that the development of digitalization exacerbates the gap in agricultural green total factor productivity among regions.

Effects of temperature on morphology, physiology, and metabolic profile of diazotrophic cyanobacteria inhabiting diverse habitats

Global population expansion has increased the demand for food supply and agricultural productivity. Abiotic stressors like temperature have significantly restricted agriculture in cropland and jeopardized food security. Cyanobacteria play a crucial role in fostering sustainable agriculture and ensuring global food security. In the present study, we have assessed the effect of temperatures on diazotrophic free living rice-field and hot-spring cyanobacteria. They were treated to a variable range of temperatures to see the changes in cellular morphology, physiology, and biochemical characteristics. The rise of temperatures induces growth (60 %), total protein (54 %) contents of rice-field cyanobacterium until 25 °C, further treatment results in decline (20 %) at 45 °C. However, growth indices were increased till 35 °C (90 %) in hot-spring cyanobacterium and further treatment did not exhibit a significant decline in the same. However, the reactive oxygen species (ROS) generation and lipid peroxidation (LPO) were higher in rice-field (2.8 and 1.7 fold) as compared to hot-spring cyanobacterium (2.2 and 1.6 fold). In response to temperature, enzymatic antioxidant contents were much higher in hot-spring as compared to rice-field cyanobacterium. Similarly, carotenoid and carbohydrate content was also higher in hot spring (2 fold) as compared to rice-field cyanobacterium (1.5 and 1.2 fold). All these data collectively suggest that hot-spring (Nostoc sp. strain VKB02) has a higher thermoprotective capacity with novel defense mechanisms as compared to rice-field cyanobacterium (Anabaena sp. strain VKB01). These findings contributed to a better understanding of the temperature stress, improvement of agricultural productivity and future welfare of green ecosystems.

Crop Yield Prediction Using Machine-Learning

This study examines the utilization of machine learning techniques for forecasting agricultural yields. This is critical because it helps to improve farm productivity and food security. Agriculture is the backbone of Pakistan’s economy, and accurate crop yield prediction can greatly assist in economic planning and allocation of resources. “Several ML algorithms, including K-Nearest Neighbor, Lasso regression, Ridge Regression, Decision Tree, and Linear Regression,” were used in this research to forecast crop yield using historical data and climate variables. Among all the models developed, the KNN algorithm was the most effective, with MAE=108023825.91 and R2=0.98494, meaning that it had the highest accuracy. These results suggest that ML has a large potential to give good predictions about crop yields, which would be useful for decision-making in agriculture and crop management or strategy development. The paper also enlightens on the limitations of some algorithms, like Decision Trees, known for their low-performance abilities; it suggests other ensemble models as better alternatives to increase predictive power. Machine learning integration with big data platforms and advanced computational methods offers a promising approach toward smart agriculture, ensuring food security.

Solar irrigation system: Analysis of the solution for livestock production in North Cameroon

This article examines the impact of implementing a solar irrigation system on livestock production in North Cameroon, a region marked by climatic challenges and increasingly dry conditions with 600 to 1,200 mm of rainfall per year and temperatures between 30°C and 40°C. Livestock farmers in the region rely heavily on natural pastures and forage crops to feed their livestock, but these resources are insufficient during periods of drought. The area is home to around 2 million head of cattle, with an estimated 500,000 to 700,000 head of sheep and 300,000 to 500,000 head of goats. Our methodology involved a comparative before-and-after analysis, where data on crop yields, pasture quality and animal performance were collected. Statistical simulation was used to assess improvements in pasture biomass, forage yield and animal performance. The results show significant improvements if the solar irrigation system is installed, with: a 30% increase in biomass; a 50% increase in forage yield with a 16.7% gain in animal weight; a 20% increase in milk production; a 30% reduction in forage procurement costs; and a 33.3% increase in income generated. These results indicate that solar irrigation will have a positive impact on farm productivity and profitability. The installation of a solar irrigation system in Northern Cameroon represents a sustainable and economically viable solution for improving livestock production in a region vulnerable to drought.In sub-Saharan Africa, solar irrigation systems have been successfully implemented in several countries, including Kenya, Ethiopia, Mali and Burkina Faso. These projects have demonstrated the viability of this technology in semi-arid and arid environments, with notable improvements in agricultural production and water management. The climatic and geographical conditions in Northern Cameroon are similar to those in the regions where solar irrigation systems have been tested.

Soil Mechanical Systems and Related Farming Machinery

The mechanization of agricultural work has contributed significantly to the improvement of agricultural productivity and reduced production costs [...]

Impact of increasing the supply of rural finance on agricultural total factor productivity: Evidence from China

We examine the impact of increasing the supply of rural finance on agricultural total factor productivity through the village banking program in China. Using county-level panel data spanning from 2000 to 2016, we apply the staggered difference-in-differences model and find a significant improvement in agricultural total factor productivity associated with the village banking program. Further mechanism analysis shows that the village banking program contributes to the improvement in agricultural total factor productivity by facilitating the migration of rural labor to urban areas and agricultural mechanization. Moreover, this effect is more pronounced in western, northern and low-income counties. This study shows that increasing the supply of rural finance can increase agricultural productivity.

Agricultural Service Trade and Green Development: A Perspective Based on China’s Agricultural Total Factor Productivity

Agricultural service trade is closely related to the promotion of the sustainable development of China’s agriculture and is necessary for comprehensive rural revitalization. To clarify the relationship between trade in agricultural services and agricultural green total factor productivity (AGTFP) from a macroscopic perspective and then analyze the acting path and threshold effect, can provide an important reference for improving agriculture’s green level and realizing the sustainable development of agriculture. This paper uses the provincial panel data of China from 2007 to 2022 to measure agricultural green total factor productivity (AGTFP) using the SBM-GML model and explores the impact of agricultural service trade on AGTFP with the help of the fixed effect model, the mediation effect model, and the threshold model. According to this study: (1) Trade in agricultural services can significantly increase AGTFP. (2) Mechanism analysis shows that trade in agricultural services can promote AGTFP through promoting industrial agglomeration, enhancing technological innovation, and improving factor allocation. (3) Heterogeneity analysis shows that trade in agricultural services has a more obvious role in promoting AGTFP in the eastern region and the main grain marketing area. (4) The threshold effect finds that the promotion of agricultural services trade on AGTFP will gradually increase as the level of government support rises. Therefore, China should actively promote the development of agricultural service trade, implement the concept of sustainable development, improve the level of government support, and promote the improvement in agricultural total factor productivity and sustainable development.

Gendered farmer perceptions towards soil nutrition and willingness to pay for a cafetière-style filter system for in-situ soil testing: Evidence from Central Kenya

Soil nutrition is a key pillar in agricultural productivity. However, point-of-need testing for soil nutrition is not readily available in resource-limited settings such as Kenya. We set out to study the perceived need for soil testing among farmers in this country. A group of 547 farmers from Murang'a and Kiambu counties in central Kenya were recruited through multi-stage sampling to help assess the perceptions and willingness to pay (WTP) toward a prototype technology for surveillance of in-situ soil nutrition. The technology is based on a cafetière-style filter system for extraction and a microfluidic paper-based analytical device (μPAD) for nutrient readout. We employed the double bounded choice contingent valuation method (CVM) to analyze the willingness of farmers to accept and pay for the prototype if the technology was available on the market. It was found that currently, only 1.5 % of farmers carry out soil testing. The high costs of analysis at testing centers, which are often far from the farmers, are among the main reasons contributing to the majority of farmers not testing their soils. The farmers surveyed were generally willing to make their soil data publicly accessible, especially to extension officers. CVM showed that uncontrolled WTP had a 94.24 % premium above KSh1,000 ($6.60) incurred by using the existing rapid testing method. Factoring the control variables and disaggregating the model into gender categories, the findings showed that youth, women, and men had WTP values of KSh1,612.53 ($10.75), KSh1,558.68 ($10.39), and KSh1,504.83 ($10.03), respectively, indicating that farmers can indeed pay for the convenience to test their soils in situ. Through the democratization of soil nutrition data, extension agents can enhance the improvement of agricultural productivity, which implies that farmers can commercialize their agricultural activities.

Comparison of Nonlinear Functions to Define the Growth in Intensive Feedlot System with XGBoost Algorithm

The aim of this study was to define the growth by using nonlinear functions in intensive feedlot system with XGBoost algorithm. To achieve this aim, five nonlinear functions were implemented. To implementation of the study, Brown Swiss (n=41) and Simental (n=95) breed were used. Each nonlinear functions were examined for each breed. According to the results of the nonlinear functions, logistic model was the best prediction model for defining the growth of each breed. In this study, the parameters in the best prediction model were calculated individually and the relationship of these parameters with body weight was evaluated with the XGBoost algorithm. Model comparison criteria such as standard deviation ratio (SDratio), Pearson’s correlation coefficient (PC), determination of coefficient (R2) and Akaike’s information criteria (AIC) were used to evaluate the XGBoost algorithm. In conclusion, the XGBoost algorithm can be an effective and optional approach that allows breeders to estimate live weight from growth parameters. This algorithm can operate on large data sets with high accuracy and speed, leading to significant improvements in agricultural productivity and animal health management. XGBoost enables more accurate predictions by analyzing the effects of various characteristics (e.g., nutritional level, breed, age). Therefore, this method can be used to determine critical parameters such as body weight in animal breeding practices, serving as a powerful support tool for operational decisions.

The effects of multiple agricultural advisory service providers on farmers’ productivity in Tanzania

ABSTRACT Despite existence of multiple agricultural advisory service providers, there is limited evidence on their contribution to farmers’ productivity. This study applied the multinomial endogenous switching regression model to analyze incremental effects of multiple advisory service providers on the partial and total factor productivities of 665 farmers in Tanzania. The study contributes to the literature by using number of service providers rather than binary or frequency measures of extension access. Results showed that farmers working with two service providers had higher improvement in the productivity of maize as well as all crops grown than those using one, three or four service providers. We recommend targeting of agricultural advisory services to meet specific needs of different crops grown by farmers in various localities, enhanced coordination and monitoring of service providers to enhance the relevance, quality and timeliness of the services provided by different actors so as to improve farm productivity levels.

Role of Agricultural Extension Services (AES) on Farm Productivity and Household Food Security in Tanzania

Abstract This study explores the effects of agricultural extension services on farm productivity and food security in Tanzania using data from the 2019/20 National Sample Census of Agriculture. The study used an Ordinary Least Square and Tobit regression models to examine the effects of agricultural extension services on agricultural productivity and food security measured in harvest per acre and Household Food Insecurity Access Scale (HFIAS) respectively. The findings highlight a significant positive correlation between using agricultural extension services and increased farm productivity and food security. These results emphasize substantial policy implications suggesting a strategic focus on enhancing the accessibility and effectiveness of extension services particularly among smallholder farmers. Furthermore, the government and stakeholders need to prioritize and invest in expanding extension services to smallholder farmers. This involves improving service quality to address the specific needs of smallholder farmers. Such approaches may contribute to improved farm productivity and food security in Tanzania.

Plant responses to abiotic stress regulated by histone acetylation.

In eukaryotes, histone acetylation and deacetylation play an important role in the regulation of gene expression. Histone acetylation levels are reversibly regulated by histone acetyltransferases (HATs) and histone deacetylases (HDACs). Increasing evidence highlights histone acetylation plays essential roles in the regulation of gene expression in plant response to environmental stress. In this review, we discussed the recent advance of histone acetylation in the regulation of abiotic stress responses including temperature, light, salt and drought stress. This information will contribute to our understanding of how plants adapt to environmental changes. As the mechanisms of epigenetic regulation are conserved in many plants, research in this field has potential applications in improvement of agricultural productivity.

The Urban-rural Income Gap, Urbanization, the Upgrading of Industrial Structure, and Agricultural Labor Productivity

Agricultural labor productivity reflects the relationship between labor factor input and economy in the agricultural industry, and improving agricultural labor productivity is an important way to achieve agricultural modernization and high-quality agricultural development. Based on the VAR model and Granger causality test, this paper conducts an empirical analysis of the impact of urban-rural income gap, urbanization, industrial structure upgrading, and agricultural labor productivity in China from 1981 to 2020, and concludes that there is a long-term cointegration relationship between urban and rural income gap, urbanization and industrial structure upgrading and agricultural labor productivity. In the long run, the urban-rural income gap and industrial structure upgrading have a positive impact on agricultural labor productivity. Urbanization has a negative impact on the improvement of agricultural labor productivity, so relevant suggestions are given.