Search Results (35)

Due to significant initial investments, adopting complex reactive irrigation technologies to manage salinity can be financially risky for farmers. This paper explores using existing irrigation systems to manage salinity by adjusting irrigation timing and amounts to manage salt and water stress. An integrated bioeconomic model, combining a crop model and an economic model, was developed to simulate the impact of irrigation decisions on crop yield and profitability. This paper used secondary data to develop the case study used in the analysis. The results indicated that the margin above specified costs for a segmented irrigation approach was consistently higher than for the uniform approach. The economic benefit varied depending on the soil salinity category that made up the uniform approach, with a maximum potential benefit of 161 ZAR/ha. Increasing irrigation in high-salinity zones to dilute salts enhanced crop yields through improved osmotic and matric potentials, leading to higher total soil water potential. Interestingly, despite higher irrigation applications, there was minimal leaching of salts. The conclusion is that farmers can effectively manage salt and water stress using their current irrigation technology, avoiding costly reactive technologies. Adjusting irrigation timing and amounts offers a viable, cost-effective solution for managing salinity and optimising crop yields. Full article

Irrigation plays a crucial role in enhancing food production, increasing land productivity, and improving the livelihoods of smallholder farmers in Sub-Saharan Africa (SSA). Solar pumps and water harvesting ponds have emerged as promising technologies for sustainable agriculture for smallholders in SSA and beyond. The socio-economic impacts of these systems are less studied in the existing literature. This study examined the agricultural productivity of solar pump and water harvesting irrigation technologies and their impacts on income and food security among smallholder farmers in the Central Rift Valley, Lake Hawassa, and Upper Awash sub-basin areas in Ethiopia. Data were collected from 161 farming households that were selected randomly from woredas where solar pump and water harvesting pond irrigation systems had been implemented. The sample size was determined using the power calculation method. Bio-physical observation and measurements were also conducted at field levels. The benefit–cost ratio (BCR) and net water value (NWV) from the use of solar pump and water harvesting pond irrigations were analyzed to assess the viability of these systems. The household food consumption score (HFCS) and household dietary diversity score (HDDS) were calculated to measure food security, while the revenue from crop production was used to measure crop income. An endogenous switching regression model was applied to address the endogeneity nature of the adoption of the irrigation technologies. The counterfactual analysis, specifically the Average Treatment Effect on the Treated (ATT), was used to evaluate the impacts of the irrigation technologies on income and food security. Results indicate that the ATT of crop income, HFCS, and HDDS are positive and statistically significant, illustrating the role of these irrigation systems in enhancing smallholder farmers’ welfare. Moreover, smallholder farmers’ solar pump irrigation systems were found to be economically viable for few crops, with a BCR greater than 1.0 and an NWV ranging from 0.21 to 1.53 USD/m³. It was also found that bundling agricultural technologies with solar pump irrigation systems leads to enhanced agricultural outputs and welfare. The sustainable adoption and scale-up of these irrigation systems demand addressing technical and financial constraints, as well as input and output market challenges. Full article

Aquaculture is the fastest-growing food industry worldwide because it allows faster intensive production in a limited space and short time. However, the trade-off of this production scheme has led to infectious disease outbreaks that harm food production with economic impacts. Immunostimulants have entered the industry to fight against diseases by enhancing the immune system and conferring better protection against pathogens. In this regard, dietary immunostimulants have been tested at the farm level, such as carbohydrates and proteins known to enhance immunity and improve survival rates under experimental conditions. Despite the success of immunostimulant inclusion in experimental aquaculture, economic evaluation is an innovative avenue to be integrated when a novel immunostimulant is developed. The bioeconomic analysis permits the accurate demonstration of advantages or disadvantages regarding survival and yield performance upon immunostimulant application through mathematical and statistical estimates. An integrative bioeconomic model for testing a novel immunostimulant should contemplate technological, biological, and economic submodels at least; thus, financial variables, such as revenue, costs, and profitability, should also be considered for proper decision-making. Therefore, this perspective briefly describes the most relevant immunostimulants used in shrimp farms and offers bioeconomic elements that should be considered for affordable immunostimulant development and inclusion in shrimp aquaculture. Full article

The integration of ecosystem services (ESs) valuation into agricultural policy frameworks is critical for fostering sustainable land management practices. This study leverages the redesigned version of the bio-economic farm model MODAM (Multi-Objective Decision Support Tool for Agro-Ecosystem Management) to estimate the shadow prices of ESs, enabling the derivation of demand and supply curves for nitrate leaching and soil erosion control, respectively. Two hypothetical farms in Brandenburg, Germany—a smaller, arable farm in Märkisch-Oderland and a larger, diversified farm with livestock in Oder-Spree—are analyzed to explore the heterogeneity in shadow prices and corresponding cropping patterns. The results reveal that larger farms exhibit greater elasticity in response to green taxes on nitrate use and lower costs for supplying erosion control compared to smaller farms. This study highlights the utility of shadow prices as proxies for setting green taxes and payments for ecosystem services (PESs), while emphasizing the need for differentiated policy designs to address disparities between farm types. This research underscores the potential of model-based ESs valuation to provide robust economic measures for policy design, fostering sustainable agricultural practices and ecosystem conservation. Full article

Cotton cultivation is resource-intensive, posing significant environmental challenges, especially with conventional farming methods. Growing interest in sustainable agriculture drives the exploration of organic farming as a potential alternative with lower environmental impacts. Despite its benefits, organic farming often faces criticism for lower crop yields, sparking debates on the trade-offs between productivity and environmental impact. This study hypothesizes that organic cotton farming will have a smaller environmental footprint and higher energy efficiency compared to conventional methods. To test this hypothesis, a cradle-to-farm gate energy analysis and life cycle assessment (LCA) were conducted on both organic and conventional seed cotton production systems in the Beheira governorate of Egypt. The ReCiPe 2016 midpoint and endpoint characterization model was used for an environmental impact assessment. The impacts were evaluated using two functional units: one ton of seed cotton and one hectare of cultivated cotton. The findings revealed that organic cotton outperforms conventional cotton in net energy gain, efficiency, and profitability, with higher productivity and lower energy intensity. Regardless of the functional unit used (mass- or land-based), the assessed organic systems generally show a better environmental performance than the conventional systems in the local context, even when accounting for data uncertainty. This is due to lower input intensity and the use of less energy-intensive organic fertilizers and bio-fertilizers. Fertilization and irrigation are key factors influencing environmental impacts, with fertilization affecting midpoint impacts and irrigation affecting endpoint impacts. Therefore, precision fertilization, efficient irrigation practices, and effective nutrient and soil moisture management are recommended to minimize environmental impacts. Subsequent studies could explore whether similar patterns are observed in different geographic regions and evaluate additional social and economic aspects of cotton sustainability beyond environmental impacts. Future agricultural LCAs should use both mass-based and area-based functional units to capture a broader range of environmental effects and evaluate the co-benefits and trade-offs between organic and conventional practices. Full article

Simulation models are used in various areas of agriculture to better understand the system and assist in decision making. In the beef production sector, a variety of simulation research focusing on various dimensions of the system is available. However, an overview of the available research is lacking. Therefore, a systematic review was conducted to provide an overview of simulation studies of beef production and create an understanding of the simulation approaches used. Scopus, Web of Science, and ProQuest Central research databases were used to search the relevant articles, with the last search conducted in June 2023. Studies that developed or used simulation strategies and used beef cattle as a primary focus of the study were included. The 105 studies included in this review were examined thoroughly to record the authors, year of publication, country of study, type of study, focus area of the study, simulated scenarios, validation methods, and software programs used. There has been growing research interest in simulating beef production systems worldwide, with most studies conducted in North America and Europe. Among these studies, the majority (84.76%, n = 89) are biophysical or bioeconomic study types and use deterministic approaches (n = 42). Additionally, most studies have a whole-farm scope (38.09%, n = 40) and focus on productivity (51.43%, n = 54). Since only less than half of the studies mentioned the validation techniques and software programs used, there is a need to improve the availability of this information to ensure that the models are adopted effectively in decision making. Full article

The bioeconomy offers an opportunity to implement a truly sustainable global economy based on biological resources, which, thanks to biotechnologies, become renewable. In this study, we conducted a bioeconomic analysis of the three most important species of sea snook in northern Sinaloa using fishery and mathematical models to support the selection of the species with the highest growth and feasibility. Our results showed a condition factor lower than 1 (K < 1) for the three species. The size condition factor was higher in younger organisms for the three snook species. The growth rates were K = 0.320, K = 0.160, and K = 0.440 for C. viridis, C. nigrescens, and C. medius, respectively. Individual growth was 1.8 g/day for C. viridis, 1.47 g/day for C. nigrescens, and 0.91 g/day for C. medius. The length-to-weight ratio indicated negative allometric growth (b = 2.82, b = 2.72, and b = 2.73, respectively) for C. viridis, C. nigrescens, and C. medius. The simulation for possible commercial cultivation reflected varied sizes: 600 g for C. viridis and C. nigrescens and 400 g for C. medius. The financial projection of C. viridis produced IRRs of 14% and 48% in captured fishing and aquaculture models, respectively, with positive NPV. However, simulations for C. nigrescens and C. medius were not economically viable. We conclude that, according to the aquaculture model, the most financially feasible species to farm in the north of Sinaloa is C. viridis, which showed the highest growth based on fishery data compared to those for C. nigrescens and C. medius. Full article

Regenerative agriculture (RA) is an approach to farming pursued globally for sustaining agricultural production and improving ecosystem services and environmental benefits. However, the lack of a standardized definition and limited bioeconomic assessments hinder the understanding and application of RA more broadly. An initial systematic review revealed a wide range of definitions for regenerative agriculture, although it is generally understood as a framework consisting of principles, practices, or outcomes aimed at improving soil health, biodiversity, climate resilience, and ecosystem function. To address existing gaps, we propose a working definition that integrates socioeconomic outcomes and acknowledges the significance of local knowledge and context to complement established scientific knowledge. A second systematic review identified indicators, tools, and models for assessing biophysical and economic aspects of RA. Additionally, a third literature review aimed to identify the potential integration of advanced analytical methods into future assessments, including artificial intelligence and machine learning. Finally, as a case study, we developed a conceptual framework for the evaluation of the bioeconomic outcomes of RA in the mixed farming setting in Australia. This framework advocates a transdisciplinary approach, promoting a comprehensive assessment of RA outcomes through collaboration, integrated data, holistic frameworks, and stakeholder engagement. By defining, evaluating assessment methods, and proposing a pragmatic framework, this review advances the understanding of RA and guides future research to assess the fit of RA practices to defined contexts. Full article

The EU’s goals by 2050 are to ensure food security, prevent bio-diversity loss, and strengthen the EU food system’s resilience. Recent scientific research and the situation in the global market show that the cultivation and processing of raspberries is currently completely unsustainable. This sector is experiencing a huge decline in Lithuania. Therefore, we chose the sustainability of raspberry growing (from farm) and processing (to fork) as an object. The aim of this article was (i) to analyze the raw material of the raspberry plant for product sustainable processing, (ii) to create a digital sustainability measurement model, and (iii) to present sustainable development solutions for effective raspberry growing and processing on Lithuanian farms using content and descriptive methods. This paper discusses how to help small raspberry growers and processors achieve sustainable economic, environmental, and social performance from field raw material to processed products. Analysis of the scientific literature has revealed qualitative and quantitative sustainability indicators for improving raspberry production. The assessment of the sustainability according to our created model revealed the (un)sustainable factors and the current situation in raspberry farms on a Likert scale from very unsustainable to very sustainable. Based on the evaluation we have determined sustainable development solutions. Raspberry growing and processing in Lithuania can contribute to environmental conservation, economic growth, and social well-being, fostering a more sustainable and resilient agricultural sector by investing in R&D, improving productivity, creating employment opportunities and supporting rural communities, establishing a robust waste management system, and embracing renewable energy sources. Raspberry growers and processors can use the digital model we created for the sustainability, efficiency, and development directions of their farm. Full article

When organic practices may have negative environmental effects, understanding the trade-offs between the economic and environmental outcomes of organic agriculture is essential in its promotion. This paper examines whether organic rice was planted in Japanese rice farms using the combined application of a multi-objective genetic algorithm and life cycle assessment. A modeled farm with 30 hectares (ha) of paddy fields is constructed using information from an agricultural management handbook. The planted crops considered are environmentally friendly rice with reduced use of chemical fertilizers and synthetic pesticides, organic rice, conventional wheat, and conventional soybeans. A bio-economic farm model with both economic and environmental objectives is created. The Pareto-optimal solutions indicate that the planted area of organic rice is 2.3 ha, at best, in the modeled farm. The cultivation of organic rice is likely to be constrained compared with that of environmentally friendly rice, and even though it produces an increase in income, it does not necessarily exert positive effects on the environment. The findings suggest that when organic farming has negative impacts, countermeasures against problems that emerge through its practice should be included in the essential requirements for subsidization. Full article

Smallholder farmers in Northern Ghana face challenges due to weather variability and market volatility, hindering their ability to invest in sustainable intensification options. Modeling can help understand the relationships between productivity, environmental, and economical aspects, but few models have explored the effects of weather variability on crop management and resource allocation. This study introduces an integrated modeling approach to optimize resource allocation for smallholder mixed crop and livestock farming systems in Northern Ghana. The model combines a process-based crop model, farm simulation model, and annual optimization model. Crop model simulations are driven by a large ensemble of weather time series for two scenarios: good and bad weather. The model accounts for the effects of climate risks on farm management decisions, which can help in supporting investments in sustainable intensification practices, thereby bringing smallholder farmers out of poverty traps. The model was simulated for three different farm types represented in the region. The results suggest that farmers could increase their income by allocating more than 80% of their land to cash crops such as rice, groundnut, and soybeans. The optimized cropping patterns have an over 50% probability of increasing farm income, particularly under bad weather scenarios, compared with current cropping systems. Full article

Ecological agriculture (E.A.) protects soil, water, and the climate, ensuring nutritious food. It encourages biodiversity and prohibits chemical inputs or hybrids. Agricultural development strategy should prioritize the development of water, land, forests, biodiversity, agricultural infrastructure, research and extension, technology transfer, investment, and unified management to bring about significant changes in agriculture. Agricultural practices have resulted in deforestation, biodiversity loss, ecosystem extinction, genetic engineering, irrigation issues, pollution, degraded soils, and related waste. Food producers increasingly use artificial neural networks (ANN) at most agricultural production and farm management stages. A new EA-ANN method, including agriculture, has been widely employed to solve categorization and prediction tasks. In addition to maintaining natural resources, sustainable agriculture helps preserve soil quality, reduces erosion, and conserves water. Ecological farming uses ecological services, including water filtering, pollination, oxygen generation, and disease and insect management. ANN increases harvest quality and accuracy of evaluating the economy by enhancing productivity. Agriculture’s prediction and economic profitability are focused on the energy optimization afforded by ANN. Ecological knowledge is assessed in light of commercial markets’ inability to provide sufficient environmental goods. Future agriculture can include robotics, sensors, aerial photos, and global positioning systems. The proposed method uses supervised artificial learning to read the data and provide an output based on effectively classifying the natural and constructed environment. The probability distribution implemented in ANN is a function specifying all possible values and probabilities of a random variable within a specific range of values. The mathematical model assumes that EA-ANN utilizes machine learning on an internet of things platform with bio-sensor assistance to achieve ecological agriculture. Microbial biotechnology is activated, and the best option for EA-ANN is calculated for an effective data-driven model. This ensures profitability and limits the impacts of manufacturing, such as pollution and waste, on the environment. Various agricultural strategies can result in environmental concerns. The EA-ANN methodology is used to make accurate predictions using field data. Agricultural workers can use the results to plan for the future of water resources more effectively. Full article

The ambition of the Dutch Ministry of Agriculture is to stimulate the transition to circular agriculture. The objective of this paper is to develop and apply a farm level model toolbox for circular-agriculture policy assessment. Transition to circular agriculture affects farm management practices and outcome in the field of finance and economics, soil quality, use of finite resources, emissions, and biodiversity. Based on this, there is a need for an integrated assessment at farm level. Therefore, Bio Economic Farm Models should be at the core of the model toolbox. Model collaboration enables answering more complex questions and enlarges the scope of the analysis. Challenges of model collaboration are among others overlapping modules, different approaches (optimisation versus simulation), and existence of different networks of model developers and users. It is argued that a governance structure and networking will foster model collaboration. To stimulate transition to more circular agriculture practices and as a demonstration, the model toolbox was applied to assess the economic and environmental impacts of a tax on N from mineral fertiliser on a representative dairy and arable farm in a region in the Netherlands. It was found that a tax on N from mineral fertiliser has relatively large income effects, while the impacts on various environmental indicators are relatively limited. Full article

In the context of a growing population, beef production is expected to reduce its consumption of human-edible food and its contribution to global warming. We hypothesize that implementing the innovations of fast rotational grazing and redesigning existing production systems using crossbreeding and sexing may reduce these impacts. In this research, the bio-economic model FarmDyn is used to assess the impact of such innovations on farm profit, workload, global warming potential, and feed-food competition. The innovations are tested in a Belgian system composed of a Belgian Blue breeder and a fattener farm, another system where calves raised in a French suckler cow farm are fattened in a farm in Italy, and third, a German dairy farm that fattens its male calves. The practice of fast rotational grazing with a herd of dairy-to-beef crossbred males is found to have the best potential for greenhouse gas reduction and a reduction of the use of human-edible food when by-products are available. Crossbreeding with early-maturing beef breeds shows a suitable potential to produce grass-based beef with little feed-food competition if the stocking rate considers the grassland yield potential. The results motivate field trials in order to validate the findings. Full article

Maintaining udder health is the primary indication for antimicrobial use (AMU) in dairy production, and modulating this application is a key factor in decreasing AMU. Defining the optimal AMU and the associated practical rules is challenging since AMU interacts with many parameters. To define the trade-offs between decreased AMU, labor and economic performance, the bioeconomic stochastic simulation model DairyHealthSim (DHS)© was applied to dairy cow mastitis management and coupled to a mean variance optimization model and marginal abatement cost curve (MACC) analysis. The scenarios included three antimicrobial (AM) treatment strategies at dry-off, five types of general barn hygiene practices, five milking practices focused on parlor hygiene levels and three milk withdrawal strategies. The first part of economic results showed similar economic performances for the blanked dry-off strategy and selective strategy but demonstrated the trade-off between AMU reduction and farmers’ workload. The second part of the results demonstrated the optimal value of the animal level of exposure to AM (ALEA). The MACC analysis showed that reducing ALEA below 1.5 was associated with a EUR 10,000 loss per unit of ALEA on average for the farmer. The results call for more integrative farm decision processes and bioeconomic reasoning to prompt efficient public interventions. Full article