Agricultural Performance Research Articles

Green Productivity in Increasing the Productivity of Sugar Cane Farmers and Reducing Impacts on the Environment


 
 
 
 
 
 
 
 
 
 This research aims to improve the productivity and environmental performance of sugarcane farming through sustainable enhancements. Utilizing the Cobb-Douglas method, factors influencing sugarcane farming management and green productivity are identified to reduce environmental impacts. The study highlights the significance of environmental aspects in the agricultural sector and proposes green productivity as a strategy to increase awareness and address land-related environmental concerns. The results show that by implementing green productivity practices, sugarcane productivity can be augmented, management costs reduced, and farmers' income increased, leading to a more sustainable and high-quality agricultural sector.
 Highlights:
 
 
 
 
 
 
 
 
 
 
 
 
 
 Increased Productivity: Implementing green productivity strategies in sugarcane farming to enhance crop yields and profitability.
 Sustainable Agriculture: Addressing environmental concerns through eco-friendly practices in sugarcane cultivation for long-term viability.
 Cobb-Douglas Method: Utilizing a robust analytical approach to identify influential factors and optimize sugarcane farming management.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Keywords: Green Productivity, Sugarcane Farming, Cobb-Douglas Analysis, Sustainability, Environmental Impact.
 
 

Green Productivity in Increasing the Productivity of Sugar Cane Farmers and Reducing Impacts on the Environment

Green Productivity in Increasing the Productivity of Sugar Cane Farmers and Reducing Impacts on the Environment

Precision technologies for the management of reproduction in dairy cows.

Precision livestock farming (PLF) utilizes information and communication technology (ICT) to continuously monitor, control, and enhance the productivity, reproduction, health, welfare, and environmental impact of livestock. Technological advancements have facilitated the seamless flow of information from animals to humans, enabling practical decision-making processes concerning health, reproduction management, and calving surveillance. With the increasing population of livestock per farm, it has become impractical for farmers to individually track every animal within these large groups. Historically, cattle management decisions heavily relied on human observation, judgment, and experience. However, it is impossible for a single individual to gather reliable audio-visual monitoring data round the clock. Presently, dairy cows exhibit subtler indicators of estrus, resulting in a substantial chance of missing an estrus cycle. Furthermore, calving complications sometimes go unnoticed on farms, resulting in a higher number of culled cattle. In addition, an increasing number of crossbred cows experience delayed return to estrus after calving due to low body condition scores (BCS). The decline in BCS during the dry period is associated with a reduced likelihood of pregnancy following the first and second postpartum inseminations. Precision technologies enable the monitoring and tracking of an individual cow's physiological behavior and reproductive parameters, thereby optimizing management practices and farm performance. Despite the exploration of various technologies, there are still some common challenges that need to be addressed, including battery lifespan, transmission range, specificity and sensitivity, storage capacity, and economic affordability. Nonetheless, the demand for these tools from farmers and researchers is growing, and the implementation of PLF in grazing systems can yield positive outcomes in terms of animal reproductive welfare and labor optimization. This review primarily focuses on the different aspects of reproduction management in dairy using sensors, automated cameras, and various computer software.

Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms

This article aimed to introduce a novel application of a hybrid particle swarm optimizer and gravitational search algorithm (HPSOGSA) that can be used for optimal control of offshore wind farms’ voltage source converter connected to HVDC transmission lines. Specifically, the algorithm was used to design fractional-order proportional-integral-derivative (FOPID) controller parameters designed to minimize the system’s objective function based on an integral squared error. The proposed FOPID controller was applied to improve offshore wind farm performance under different transient conditions, and its results were compared with a PI controller that was designed using a genetic algorithm and grey wolf optimization algorithm. The fault ride-through capabilities of the proposed control strategy were also evaluated. The findings suggest that the HPSOGSA-based FOPID controller outperformed the other two methods, significantly enhancing offshore wind farm operations. The control strategy was thoroughly tested using MATLAB/Simulink under various operating scenarios.

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.

A wind tunnel study on cyclic yaw control: Power performance and wake characteristics

In this study, wind-tunnel experiments are used to investigate cyclic yaw control as a wake mitigation strategy aiming to improve wind farm power production. The control strategy is applied to a single wind turbine and a wind farm comprising of a column of three wind turbines in full wake state. The power performance of the wind farm is optimized by adjusting the control parameters, which include the yaw angle amplitude and yaw Strouhal number, and the wake flows are investigated using particle-image velocimetry measurements. The yaw angle of the controlled turbine is varied dynamically in a sinusoidal manner. A power gain of more than 10 % compared to an uncontrolled wind farm is achieved when the yaw angle amplitude and yaw Strouhal number are in the range of 10°–35° and 0.09-0.22, respectively, with a peak power gain of 15.2 %. The power consumed to generate the control is found to be small and predictable. Results from wake measurements reveal that by deflecting the wake cyclically, this control strategy can lead to relatively flattened mean wake velocity deficit profiles, faster wake recovery and very different wake turbulence intensity distributions, compared to the uncontrolled case. The results show that cyclic yaw control has a great potential in improving the power production of wind farms. A scaling of the control parameters based on the kinematic similarity between the wind-tunnel scale and utility-scale wind farms is also proposed.

Analysis of low voltage ride-through capability and optimal control strategy of doubly-fed wind farms under symmetrical fault

Given the “carbon neutralization and carbon peak” policy, enhancing the low voltage ride-through (LVRT) capability of wind farms has become a current demand to ensure the safe and stable operation of power systems in the context of a possible severe threat of large-scale disconnection caused by wind farms. Currently, research on the LVRT of wind farms mainly focuses on suppressing rotor current and providing reactive current support, while the impact of active current output on LVRT performance has not been thoroughly discussed. This paper studies and reveals the relationship between the limit of reactive current output and the depth of voltage drop during LVRT for doubly-fed induction generator (DFIG) based wind farms. Specifically, the reactive current output limit of the grid-side converter is independent of the depth of voltage drop, and its limit is the maximum current allowed by the converter, while the reactive current output limit of the DFIG stator is a linear function of the depth of voltage drop. An optimized scheme for allocating reactive current among the STATCOM, DFIG stator, and grid-side converter is proposed. The scheme maximizes the output of active current while satisfying the standard requirements for reactive current output. Compared to traditional schemes, the proposed LVRT optimization strategy can output more active power during the LVRT period, effectively suppressing the rate of rotor speed increase, and improving the LVRT performance and fault recovery capability of wind farms. Simulation results verify the effectiveness of the proposed scheme.

Evaluating the effects of grass management technologies on the physical, environmental, and financial performance of Irish pasture-based dairy farms

Grass management technologies (grass measuring devices and grassland management decision support tools) have been identified as important tools to improve the performance of pasture-based dairy farms. They have the potential to significantly improve the efficiency and sustainability of dairy systems by increasing milk production through enhanced pasture growth and utilization, which would reduce the need for supplementary feeds, along with increased output, therefore increasing farm profitability and environmental sustainability. Despite the several potential benefits of grass management technologies, there is a lack of empirical research around the effects of these technologies on the performance of pasture-based dairy systems. The current study aimed to fill this knowledge gap by using a 2018 nationally representative survey of Irish dairy farms and a propensity score matching approach to determine the effects of adopting grass management technologies on the physical, environmental, and financial performance of Irish pasture-based dairy farms. The findings showed that dairy farms utilizing grass management technologies had, on average, higher farm physical, environmental, and financial performance (in terms of grazed pasture use, total pasture use, length of the grazing season, milk yield, milk solids, greenhouse gas emissions per kilogram of fat- and protein-corrected milk, gross output, and gross margin) compared with dairy farms not utilizing these technologies. However, when controlling for selection bias, we can only attribute a positive causal effect of grass management technology adoption on the use of grazed pasture per cow, grazing season length, milk yield per cow, and milk solids per cow. This might be due to dairy farmers not yet using the technologies to their full potential, 2018 being an unusual year in terms of weather (and therefore not being able to capture the full range of farm performance benefits), or because grass management technologies need to be adopted in association with other technologies and practices to achieve their expected performance outcomes. Future research should include updated farm-level data to capture the weather and learning effects and so be able to determine the impact of grass management technologies on a wider range of performance indicators.

A dynamic multi-stage network data envelopment analysis approach for evaluating performance of wheat farms

PurposeBecause of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this paper is to develop a data envelopment analysis (DEA) model that is fully compatible with the wheat production process so that managers and farmers can use it to evaluate the efficiency of wheat farms for strategic decisions.Design/methodology/approachA dynamic multi-stage network DEA model is developed to evaluate the efficiency of wheat production farms in short-term (two-year) and long-term (eight-year) periods.FindingsThe results of this study show that because of the lack of long-term planning and excessive reliance on rain, most of the investigated regions have no stability in efficiency, and the efficiency of the regions changes in a zigzag manner over time. Among studied regions, only the Hashtrood region has high and stable efficiency, and other regions can follow the example of this region's cultivation method.Originality/valueTo the best of the authors’ knowledge, this study is the first one that uses the dynamic multi-stage network DEA considering every other year cultivation method and direct–indirect inputs in the agricultural section.

Modelling the impact of trapped lee waves on offshore wind farm power output

Abstract. Mesoscale meteorological phenomena, including atmospheric gravity waves (AGWs) and including trapped lee waves (TLWs), can result from flow over topography or coastal transition in the presence of stable atmospheric stratification, particularly with strong capping inversions. Satellite images show that topographically forced TLWs frequently occur around near-coastal offshore wind farms. Yet current understanding of how they interact with individual turbines and whole farm energy output is limited. This parametric study investigates the potential impact of TLWs on a UK near-coastal offshore wind farm, Westermost Rough (WMR), resulting from westerly–southwesterly flow over topography in the southeast of England. Computational fluid dynamics (CFD) modelling (using Ansys CFX) of TLW situations based on real atmospheric conditions at WMR was used to better understand turbine level and whole wind farm performance in this parametric study based on real inflow conditions. These simulations indicated that TLWs have the potential to significantly alter the wind speeds experienced by and the resultant power output of individual turbines and the whole wind farm. The location of the wind farm in the TLW wave cycle was an important factor in determining the magnitude of TLW impacts, given the expected wavelength of the TLW. Where the TLW trough was coincident with the wind farm, the turbine wind speeds and power outputs were more substantially reduced compared with when the TLW peak was coincident with the location of the wind farm. These reductions were mediated by turbine wind speeds and wake losses being superimposed on the TLW. However, the same initial flow conditions interacting with topography under different atmospheric stability settings produce differing near-wind-farm flow. Factors influencing the flow within the wind farm under the different stability conditions include differing, hill and coastal transition recovery, wind farm blockage effects, and wake recovery. Determining how much of the differences in wind speed and power output in the wind farm resulted from the TLW is an area for future development.

Supporting the implementation and monitoring of selective dry cow therapy (SDCT) on NY State dairy farms

Selective dry cow therapy (SDCT) is both a cost-saving and an­timicrobially conscientious approach to the critical time of dry off in a dairy cow’s lactation. Guidance regarding proper imple­mentation is crucial to the success of a SDCT program, and farm and cow-level selection parameters should be carefully followed. The objective of this project was to create a formal program in which eligible dairy farms in New York State would receive guidance in the form of SDCT protocol development, software use, dry-off procedure training and data monitoring regarding udder health and milk quality parameters through the entire enrollment period. Here we describe the program and farm performance after implementation thus far.

Identifying and assessing intensive and extensive technologies in European dairy farming

Abstract In order to tackle climate change and biodiversity loss, the European Union (EU) promotes extensive farming. However, identifying such farms across countries and assessing their performance for policy purposes remains challenging. This paper combines a latent class stochastic frontier model (LCSFM) with a novel nested metafrontier approach. The resulting model enables the identification of intensive and extensive farms across countries, estimation of farm efficiency and identification of different technology gaps. Based on Farm Accountancy Data Network data of French, Irish and Austrian dairy farms,we find poorer environmental but better economic performance of intensive farms, compared to extensive farms. The largest productivity differences stem from technology gaps and not from inefficiency. The approach enables a more nuanced analysis of sources of inefficiency to assist policy design for future green payments in the EU.

Data‐driven modelling of turbine wake interactions and flow resistance in large wind farms

AbstractTurbine wake and local blockage effects are known to alter wind farm power production in two different ways: (1) by changing the wind speed locally in front of each turbine and (2) by changing the overall flow resistance in the farm and thus the so‐called farm blockage effect. To better predict these effects with low computational costs, we develop data‐driven emulators of the ‘local’ or ‘internal’ turbine thrust coefficient as a function of turbine layout. We train the model using a multi‐fidelity Gaussian process (GP) regression with a combination of low (engineering wake model) and high‐fidelity (large eddy simulations) simulations of farms with different layouts and wind directions. A large set of low‐fidelity data speeds up the learning process and the high‐fidelity data ensures a high accuracy. The trained multi‐fidelity GP model is shown to give more accurate predictions of compared to a standard (single‐fidelity) GP regression applied only to a limited set of high‐fidelity data. We also use the multi‐fidelity GP model of with the two‐scale momentum theory (Nishino & Dunstan 2020, J. Fluid Mech. 894, A2) to demonstrate that the model can be used to give fast and accurate predictions of large wind farm performance under various mesoscale atmospheric conditions. This new approach could be beneficial for improving annual energy production (AEP) calculations and farm optimization in the future.

Analysis of Performance Factors According to the Operation of Care Farms

Background and objective: This study was conducted to understand the current status and performance factors of domestic agricultural management in order to develop a business model that meets the needs of the times along with the newly emerging demand for agro-healingMethods: An online survey was conducted on 170 care farming business entities nationwide, and the collected data were frequency-coded using SPSS Ver.25. An exploratory factor analysis was conducted to extract performance factors, and a reliability analysis was performed to verify internal consistency for each factor.Results: The factors that determine the performance of care farms, such as sales and profits, were classified into seven performance determinants (X1 to X7). Factor 1 was classified as one factor by grouping X2(excellence in use of animals/ plants), X6 (program excellence), X5(quality of facility), and X3 (size of facility) together, and Factor 2 was classified as one factor by grouping two items, X7 (governmental support) and X1(location) together, showing high internal consistency between all Cronbach’s α. The correlation between the factors determining care farm performance did not have statistically significant results between X7 (governmental support), X2 (excellence in use of animals/ plants), and X6 (excellence in program), but overall, there was a correlation between items that was statistically significant. A regression analysis was conducted to determine the variables affecting the performance factors of care farms, and at the 95% confidence level, both the location and sales of care farms were statistically significant.Conclusion: Care farms that provide care farming services were often operated on a small scale because they didn’t have a sufficient base of profit, with the exception of farms operated in connection with former rural education farms. In the future, it is considered for care farms necessary to utilize factors of regional differentiation, such as the locational environment and the characteristics of the region, or to link with multiple targets and institutions in the region.

Determinants of commercial farmers’ willingness to mentor emerging farmers in South African land reform

ABSTRACT To address the challenge posed by a lack of commercial farming skills among beneficiaries of redistributed farms, the South African Government has introduced a mentorship program, by which established commercial farmers share expertise with newly settled farmers. However, commercial farmers’ willingness to mentor emerging farmers, and the determinants of their willingness at a national level, are not explicitly known. This article attempts to contribute in this regard by implementing a binary logistic regression model, preceded by descriptive statistics, to a sample of 475 commercial farmers. Our findings show that a majority (76 per cent) of commercial farmers are willing to mentor emerging farmers. Younger farmers with higher levels of education are most likely to show willingness to mentor. Farm characteristics, such as high turnover and low perceived threat regarding a “lack of dependable labour” also contribute to the willingness to mentor. Therefore, a farmer and his or her farm performance should be key attributes in the criteria for mentor selection.

Racial disparities in farm loan application processing: Are Black farmers disadvantaged?

AbstractBlack farmers have historically been discriminated against in services from the federal government, including access to credit. Discrimination can take the form of delayed loan processing requests, which can affect timely planting, harvesting, feeding of livestock, and farm performance. This study uses nationwide, farm‐level data from 2009 to 2021 from the Farm Service Agency's direct farm loan program to investigate racial discrimination in the farm loan program. Findings reveal loan processing times average longer for Black borrowers on operating loans, though with a substantial state‐level variation. Specifically, it takes an average of more than two additional days for Black farmers' operating loan applications to be completed and another two additional days to be processed. However, there was no significant difference in time for farm ownership loans suggesting a more nuanced cause than outright racial discrimination. Other factors that increased loan processing time for borrowers included larger loan amounts, more complex loan types, a mix of collateral, and being a new borrower.

Evaluating Digital Agriculture Recommendations with Causal Inference

In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of smart farming tools. Even though recent advancements in AI-driven digital agriculture can offer high-performing predictive functionalities, they lack tangible quantitative evidence on their benefits to the farmers. Field experiments can derive such evidence, but are often costly, time consuming and hence limited in scope and scale of application. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators (e.g., yield in this case). This way, we can increase farmers' trust via enhancing the transparency of the digital agriculture market, and in turn accelerate the adoption of technologies that aim to secure farmer income resilience and global agricultural sustainability against a changing climate. As a case study, we designed and implemented a recommendation system for the optimal sowing time of cotton based on numerical weather predictions, which was used by a farmers' cooperative during the growing season of 2021. We then leverage agricultural knowledge, collected yield data, and environmental information to develop a causal graph of the farm system. Using the back-door criterion, we identify the impact of sowing recommendations on the yield and subsequently estimate it using linear regression, matching, inverse propensity score weighting and meta-learners. The results revealed that a field sown according to our recommendations exhibited a statistically significant yield increase that ranged from 12% to 17%, depending on the method. The effect estimates were robust, as indicated by the agreement among the estimation methods and four successful refutation tests. We argue that this approach can be implemented for decision support systems of other fields, extending their evaluation beyond a performance assessment of internal functionalities.

Analysis of Wind Farms under Different Yaw Angles and Wind Speeds

Wind farm optimization is pivotal in maximizing energy output, reducing costs, and minimizing environmental impact. This study comprehensively explores wind farm behavior under varying wind conditions and yaw angles to achieve these objectives. The primary motivation is to optimize wind farm performance and efficiency through proper yaw adjustment in response to wind speed changes. A computational investigation using a three-by-three wind turbine array was conducted, employing large eddy simulation (LES) to evaluate wind farm performance. Nine LES cases were considered, incorporating three wind speeds (7.3 ms−1, 10.4 ms−1, and 4.3 ms−1) and three yaw angles (30°, 20°, and 0°), with nearly constant turbulence intensity (TI) at 12.0%. The impact of wind speed and yaw angles on wake characteristics and power outputs were analyzed. The findings reveal that wind speed has limited influence on wake characteristics and power outputs, except for lower wind speeds at a yaw angle of 20 degrees. These results contribute to understanding wind farm performance optimization, aiding in developing strategies to enhance energy extraction while minimizing costs and environmental implications.

Environmental life cycle assessment of a large-scale commercial vertical farm

Vertical farms are often claimed to have better environmental performance compared to conventional products on the market. However, there are few assessments of the environmental performance of vertical farms, in particular commercial systems. This study has aimed to analyze the environmental performance of a large commercial vertical farm in Sweden producing packaged lettuce. Life cycle assessment was employed to assess the environmental performance and compare it to conventional imported and domestically produced lettuce from a cradle-to-grave perspective. For conventional supply chains, the production, transportation to Sweden, washing, processing, packaging, and final distribution were all included. The results from the study suggest that the vertical farm has lower GHG emissions than conventionally sourced varieties. However, for other impact categories, the vertical farm may have larger environmental impacts from a life cycle perspective due primarily to the large electricity demand. The electricity use, packaging, infrastructure, and distribution of the products to retail were found to be the primary hotspots for the system. Finally, key performance indicators are also provided to compare the results of this study with other indoor farming studies. The results from the KPIs suggest that the vertical farm has better water use efficiency, GHG emissions, and energy use efficiency compared to other studies in the literature. The results of this study provide insights into the environmental performance and resource use of vertical farms, which can be used for comparisons and validation of claims in the industry, and to provide empirical evidence to this developing field.

The water, land and carbon footprint of conventional and organic dairy systems in the Netherlands and Spain. A case study into the consequences of ecological indicator selection and methodological choices

Dairy farming systems are multifunctional processes that provide milk but also beef, veal and manure. These outputs provided by dairy farms are important foods for humans but their production require natural resources like water and land, and release emissions to the water and air contributing to climate change. Many studies quantified the environmental performance of dairy farms by using a life cycle assessment (LCA) or environmental footprint calculation. This study provides a better understanding of how different methodological decisions (e.g., the choice of system boundary, GHG metric, allocation procedure for multifunctionality, and multi-environmental indicators) influence the environmental performance calculation. From a footprinting point of view, the water footprints (WFs) (i.e., green, blue and grey), land footprints (LFs) and carbon footprints (CFs) of milk, beef and veal produced in two conventional (Dutch and Spanish) and an organic Dutch dairy system are estimated. Here the system boundaries are expanded so calve systems are included. Next, the use of different indicators is discussed, e.g., green WFs and the GWP100 or GWP20. The Dutch conventional system has relatively small footprints due to high efficiency. Green, blue and grey WFs per kg of milk are 0.62, 0.09 and 0.14 m3. The Spanish system has green, blue and grey WFs per kg of milk of 0.67, 0.15 and 0.09 m3; the Dutch organic system of 0.84, 0.13 and 0.26 m3. The Spanish system has the largest LF and CF, caused by feed import from countries with relatively low yields and transport greenhouse gas emissions. Dutch systems use more locally produced feed. Due to lower efficiency, the organic system has larger footprints than the Dutch conventional system. Expanding system boundaries to include calves results in an 8 to 15% CF increase. Green water dominates total WFs, an aspect excluded in LCA studies. For grey WFs, earlier studies only included nitrogen. However, if also pesticides would be included, results might be less favourable for systems relying on feed crops instead of grasslands. Also, water quality standards influence grey WFs. The study emphasizes that indicator choice influences final results. Indicators like animal welfare, biodiversity or pesticide use give different outcomes which might be more favourable for organic production.