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AgriEngineering
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Energy and Water Consumption in Agriculture: Use of Statistical Analysis...
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Energy and Water Consumption in Agriculture: Use of Statistical Analysis and Machine-Learning Methods

A special issue of AgriEngineering (ISSN 2624-7402).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 13917

Special Issue Editors

Dr. Michael D. Murphy

E-Mail Website
Guest Editor
Department of Process, Energy and Transport Engineering, Cork Institute of Technology, Cork, Ireland
Interests: agricultural engineering; building simulation; virtual laboratories for higher education; energy systems modelling; optimization; machine learning; microgrids; dairy production optimization; demand side management; energy storage
Special Issues, Collections and Topics in MDPI journals
Dr. John Upton

E-Mail Website
Guest Editor
Animal and Grassland Research and Innovation Centre, Teagasc Moorepark Fermoy, P61 C996 Cork, Ireland
Interests: direct and indirect energy consumption of dairy farms; smart metering networks and demand side management in agriculture; renewable energy integration and management; water consumption on dairy farms; milking machine performance and milking management
Dr. Paria Sefeedpari

E-Mail Website
Guest Editor
Wageningen Livestock Research, Wageningen University and Research, P.O. Box 338, 6700 AH Wageningen, The Netherlands
Interests: agricultural engineering; agricultural systems; energy consumption; energy cost of production; environmental impact; agriculture and environment; manure treatment; modeling; optimization methods; renewable resources; sustainable livestock farming; project management; nutrients cycle
Dr. Philip Shine

E-Mail Website
Guest Editor
Department of Process, Energy and Transport Engineering, Cork Institute of Technology, Cork, Ireland
Interests: energy; water; machine learning; agricultural engineering; decision support systems; demand side management

Special Issue Information

Dear Colleagues,

Increased agricultural production to feed a growing global population will result in an increased requirement for energy and water resources. The use of direct energy (e.g. grid-sourced electricity, liquid/gaseous fossil energy), ancillary energy (e.g. fertilizer and animal feed use), and embodied energy (e.g. in buildings and machinery) in agriculture are all responsible for the emission of greenhouse gases which are negatively impacting the global climate.  Accounting for 70% of all global freshwater demand (across green, blue and grey water categories), agricultural activities are the largest consumer of fresh water globally. Therefore, the agricultural sector requires substantial improvements in water-use efficiency and productivity, as increasing the production of agricultural products is limited by land availability and the availability of freshwater. Simultaneously, the agricultural sector must reduce its dependence on fossil fuels to ensure the future sustainability of agricultural production. Statistical analysis, water/carbon/energy footprint assessments, predictive modelling and machine-learning methods can offer farmers, members of the scientific community and policy makers a greater understanding of agricultural related energy and water use, to help improve its overall sustainability through informed decision making. Predictive modelling may also remove time and monetary constraints associated with the physical monitoring of energy and water use allowing for life-cycle assessments to be carried out on a larger scale. Across the pastoral and arable farming literature, various analysis, predictive modelling and optimization methods have been employed, including, but not limited to, life cycle assessment, classification and regression modelling, support vector machine, artificial neural network, random forest, genetic algorithm, particle swarm optimization, dynamic programming and accompanying methodologies such as outlier/anomaly detection and feature selection. Machine learning methods have been shown to improve prediction accuracy when compared to standard statistical approaches, thus improving stakeholder confidence in their outputs and/or recommendations. Statistical analyses have the ability to identify relationships and differences in resource consumption between agricultural processes, while life cycle assessments and optimization methods can help identify strategies to help improve overall energy/water productivity. However, these methods have not yet been utilized to their maximum potential with regard to agricultural energy and water utilization. In doing so, open-source knowledge sharing will help accelerate our efforts towards sustainable agricultural production, as barriers to information and understanding are removed.

This Special Issue aims to publish the latest developments related to the statistical analysis, energy/water footprint assessments, modeling/simulation and optimization of energy and water utilization in the agricultural domain. The topics include, but are not limited to, the following:

  • Energy–water–food nexus
  • Life cycle assessment
  • Carbon footprint assessment
  • Water footprint assessment
  • Pastoral and arable farming
  • Dairy farming
  • Livestock farming
  • Grassland farming
  • Direct and indirect energy and water utilization
  • Regression and classification analysis
  • Demand side management
  • Statistical analysis
  • Prediction modelling
  • Multiple linear regression
  • Optimization
  • Machine learning
  • Artificial intelligence
  • Deep learning
  • Anomaly detection
  • Supervised and unsupervised learning
  • Feature selection and analysis

Dr. Michael D. Murphy
Dr. John Upton
Dr. Paria Sefeedpari
Dr. Philip Shine
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AgriEngineering is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • energy
  • water
  • machine learning
  • artificial intelligence
  • statistical analysis
  • regression
  • classification
  • agriculture

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Published Papers (5 papers)

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Research

17 pages, 1321 KiB  
Article
Modelling the Temperature Inside a Greenhouse Tunnel
by Keegan Hull, Pieter Daniel van Schalkwyk, Mosima Mabitsela, Ethel Emmarantia Phiri and Marthinus Johannes Booysen
AgriEngineering 2024, 6(1), 285-301; https://doi.org/10.3390/agriengineering6010017 - 25 Jan 2024
Viewed by 1736
Abstract
Climate-change-induced unpredictable weather patterns are adversely affecting global agricultural productivity, posing a significant threat to sustainability and food security, particularly in developing regions. Wealthier nations can invest substantially in measures to mitigate climate change’s impact on food production, but economically disadvantaged countries face [...] Read more.
Climate-change-induced unpredictable weather patterns are adversely affecting global agricultural productivity, posing a significant threat to sustainability and food security, particularly in developing regions. Wealthier nations can invest substantially in measures to mitigate climate change’s impact on food production, but economically disadvantaged countries face challenges due to limited resources and heightened susceptibility to climate change. To enhance climate resilience in agriculture, technological solutions such as the Internet of Things (IoT) are being explored. This paper introduces a digital twin as a technological solution for monitoring and controlling temperatures in a greenhouse tunnel situated in Stellenbosch, South Africa. The study incorporates an aeroponics trial within the tunnel, analysing temperature variations caused by the fan and wet wall temperature regulatory systems. The research develops an analytical model and employs a support vector regression algorithm as an empirical model, successfully achieving accurate predictions. The analytical model demonstrated a root mean square error (RMSE) of 2.93 °C and an R2 value of 0.8, while the empirical model outperformed it with an RMSE of 1.76 °C and an R2 value of 0.9 for a one-hour-ahead simulation. Potential applications and future work using these modelling techniques are then discussed. Full article
(This article belongs to the Special Issue Energy and Water Consumption in Agriculture: Use of Statistical Analysis and Machine-Learning Methods)
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24 pages, 7226 KiB  
Article
An Artificial Neural Network for Predicting Groundnut Yield Using Climatic Data
by Hirushan Sajindra, Thilina Abekoon, Eranga M. Wimalasiri, Darshan Mehta and Upaka Rathnayake
AgriEngineering 2023, 5(4), 1713-1736; https://doi.org/10.3390/agriengineering5040106 - 30 Sep 2023
Cited by 7 | Viewed by 2595
Abstract
Groundnut, being a widely consumed oily seed with significant health benefits and appealing sensory profiles, is extensively cultivated in tropical regions worldwide. However, the yield is substantially impacted by the changing climate. Therefore, predicting stressed groundnut yield based on climatic factors is desirable. [...] Read more.
Groundnut, being a widely consumed oily seed with significant health benefits and appealing sensory profiles, is extensively cultivated in tropical regions worldwide. However, the yield is substantially impacted by the changing climate. Therefore, predicting stressed groundnut yield based on climatic factors is desirable. This research focuses on predicting groundnut yield based on several combinations of climatic factors using artificial neural networks and three training algorithms. The Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient algorithms were evaluated for their performance using climatic factors such as minimum temperature, maximum temperature, and rainfall in different regions of Sri Lanka, considering the seasonal variations in groundnut yield. A three-layer neural network was employed, comprising a hidden layer. The hidden layer consisted of 10 neurons, and the log sigmoid functions were used as the activation function. The performance of these configurations was evaluated based on the mean squared error and Pearson correlation. Notable improvements were observed when using the Levenberg–Marquardt algorithm as the training algorithm and applying the natural logarithm transformation to the yield values. These improvements were evident through the higher Pearson correlation values for training (0.84), validation (1.00) and testing (1.00), and a lower mean squared error (2.2859 × 10−21) value. Due to the limited data, K-Fold cross-validation was utilized for optimization, with a K value of 5 utilized for the process. The application of the natural logarithm transformation to the yield values resulted in a lower mean squared error (0.3724) value. The results revealed that the Levenberg–Marquardt training algorithm performs better in capturing the relationships between the climatic factors and groundnut yield. This research provides valuable insights into the utilization of climatic factors for predicting groundnut yield, highlighting the effectiveness of the training algorithms and emphasizing the importance of carefully selecting and expanding the climatic factors in the modeling equation. Full article
(This article belongs to the Special Issue Energy and Water Consumption in Agriculture: Use of Statistical Analysis and Machine-Learning Methods)
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15 pages, 1030 KiB  
Article
Energy Balance Assessment in Agricultural Systems; An Approach to Diversification
by Susanthika Dhanapala, Helitha Nilmalgoda, Miyuru B. Gunathilake, Upaka Rathnayake and Eranga M. Wimalasiri
AgriEngineering 2023, 5(2), 950-964; https://doi.org/10.3390/agriengineering5020059 - 26 May 2023
Viewed by 2843
Abstract
The energy in agricultural systems is two-fold: transformation and utilization. The assessment and proper use of energy in agricultural systems is important to achieve economic benefits and overall sustainability. Therefore, this study was conducted to evaluate the energy balance of crop and livestock [...] Read more.
The energy in agricultural systems is two-fold: transformation and utilization. The assessment and proper use of energy in agricultural systems is important to achieve economic benefits and overall sustainability. Therefore, this study was conducted to evaluate the energy balance of crop and livestock production, net energy ratio (NER), and water use efficiency (WUE) of crops of a selected farm in Sri Lanka using the life cycle assessment (LCA) approach. In order to assess the diversification, 18 crops and 5 livestock types were used. The data were obtained from farm records, personal contacts, and previously published literature. Accordingly, the energy balance in crop production and livestock production was −316.87 GJ ha−1 Year−1 and 758.73 GJ Year−1, respectively. The energy related WUE of crop production was 31.35 MJ m−3. The total energy balance of the farm was 736.2 GJ Year−1. The results show a negative energy balance in crop production indicating an efficient production system, while a comparatively higher energy loss was shown from the livestock sector. The procedure followed in this study can be used to assess the energy balance of diversified agricultural systems, which is important for agricultural sustainability. This can be further developed to assess the carbon footprint in agricultural systems. Full article
(This article belongs to the Special Issue Energy and Water Consumption in Agriculture: Use of Statistical Analysis and Machine-Learning Methods)
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12 pages, 1498 KiB  
Article
Potential of Grid-Connected Photovoltaic Systems in Brazilian Dairy Farms
by Antonio José Steidle Neto, Daniela de Carvalho Lopes and Sheila Tavares Nascimento
AgriEngineering 2022, 4(1), 122-133; https://doi.org/10.3390/agriengineering4010008 - 3 Feb 2022
Cited by 2 | Viewed by 2605
Abstract
The insufficient supply of electrical energy, in addition to frequent disturbances and interruptions, has motivated the inclusion of solar, biogas, biomass or wind energy systems in many Brazilian farms. However, there are few studies that have addressed the technical and economic impacts of [...] Read more.
The insufficient supply of electrical energy, in addition to frequent disturbances and interruptions, has motivated the inclusion of solar, biogas, biomass or wind energy systems in many Brazilian farms. However, there are few studies that have addressed the technical and economic impacts of renewable sources for generating electricity in rural applications, leading farmers not to invest in these technologies for fear of financial losses. This study was carried out to evaluate the potential of grid-connected photovoltaic systems for supplying the electricity demand in dairy farms located at Minas Gerais State, Brazil. The electricity generated by grid-connected photovoltaic systems was estimated from global solar radiation measurements, considering six municipalities of Minas Gerais State, Brazil. Electricity consumption was monitored monthly during one year in 12 farms. The average percentages of electricity consumption in the main operations executed at farms were 4, 27, 12, 33 and 24% for lighting, milking, cleaning/disinfection (water heating and pumping), milk cooling/refrigeration and miscellaneous, respectively. The monthly differences between the electricity generation and consumption for the studied municipalities demonstrated the technical feasibility of grid-connected systems installed directly in the dairy farms, helping to achieve energy sustainability. Full article
(This article belongs to the Special Issue Energy and Water Consumption in Agriculture: Use of Statistical Analysis and Machine-Learning Methods)
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12 pages, 1654 KiB  
Article
Assessing the Effect of Modifying Milking Routines on Dairy Farm Economic and Environmental Performance
by Michael Breen, Michael D. Murphy and John Upton
AgriEngineering 2021, 3(2), 266-277; https://doi.org/10.3390/agriengineering3020018 - 18 May 2021
Cited by 3 | Viewed by 2612
Abstract
The objective of this paper was to quantify the economic and environmental effects of changing a dairy farm’s milking start times. Changing morning and evening milking start times could reduce both electricity costs and farm electricity related CO2 emissions. However, this may [...] Read more.
The objective of this paper was to quantify the economic and environmental effects of changing a dairy farm’s milking start times. Changing morning and evening milking start times could reduce both electricity costs and farm electricity related CO2 emissions. However, this may also involve altering farmer routines which are based on practical considerations. Hence, these changes need to be quantified both in terms of profit/emissions and in terms of how far these milking start times deviate from normal operations. The method presented in this paper optimized the combination of dairy farm infrastructure setup and morning and evening milking start times, based on a weighting variable (α) which assigned relative importance to labor utilization, farm net profit and farm electricity related CO2 emissions. Multi-objective optimization was utilized to assess trade-offs between labor utilization and net profit, as well as labor utilization and electricity related CO2 emissions. For a case study involving a 195 cow Irish dairy farm, when the relative importance of maximizing farm net profit or minimizing farm electricity related CO2 emissions was high, the least common milking start times (06:00 and 20:00) were selected. When the relative importance of labor utilization was high, the most common milking start times (07:00 and 17:00) were selected. The 195 cow farm saved €137 per annum when milking start times were changed from the most common to the least common. Reductions in electricity related CO2 emissions were also seen when the milking start times were changed from most common to least common. However, this reduction in emissions was primarily due to the addition of efficient and renewable technology to the farm. It was deduced that the monetary and environmental benefits of altering farmer milking routines were unlikely to change normal farm operating procedures. Full article
(This article belongs to the Special Issue Energy and Water Consumption in Agriculture: Use of Statistical Analysis and Machine-Learning Methods)
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