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Smart Farming and Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: 26 April 2024 | Viewed by 91459

Special Issue Editor

Dr. Claus G. Sørensen

E-Mail Website
Guest Editor
Department of Engineering, Faculty of Science and Technology, Aarhus University, 8200 Århus N, Denmark
Interests: operations and production management; information and communication technology; smart farming, system analyses; sustainability of innovative technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture is approaching an era of Smart Farming, where farming operations become digitalized and data-driven, enabling advanced decision support, smart analyses and planning, etc. This can be used not only for controlling and optimizing individual operations but also for analyses and planning of the whole farming system. Smart Farming makes it possible to monitor and control many diversified and interconnected biological and technical parameters through an increasing number of automated devices and tools interacting with the need to track the production processes for operations optimization or traceability toward consumers. Additionally, improved sensing and monitoring of the production reduce the environmental impact, increase quality, quantity, and, more importantly, overall sustainability. Smart Farming gives a unique possibility to assess and evaluate agricultural sustainability with an unprecedented level of accuracy and precision.   

In this Special Issue, we are open to contributions (research papers and a limited number of reviews) exploring the development and advancement of sustainability in Smart Farming, including agri-food supply chains. This includes using the tools of Smart farming to assess, evaluate, and control the sustainability of innovative agri-food systems (innovative technologies, digitized operations and production systems, autonomous systems, etc.). Contributions describing sustainability of integrated farming systems (digital farming concepts connected with Internet of Things (IoT) technologies providing automatic input/output for the production systems) are also welcome.

Dr. Claus G. Sørensen
Guest Editor

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. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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

  • Smart Farming
  • Operations and production management
  • Information management systems
  • Digitalization
  • Sustainability assessment and evaluation

Published Papers (16 papers)

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Research

Jump to: Review, Other

28 pages, 3134 KiB  
Article
Digitally-Enabled Crop Disorder Management Process Based on Farmer Empowerment for Improved Outcomes: A Case Study from Sri Lanka
by Janagan Sivagnanasundaram, Jeevani Goonetillake, Rifana Buhary, Thushara Dharmawardhana, Renuka Weerakkody, Rukmali Gunapala and Athula Ginige
Sustainability 2021, 13(14), 7823; https://0-doi-org.brum.beds.ac.uk/10.3390/su13147823 - 13 Jul 2021
Cited by 2 | Viewed by 2406
Abstract
We have developed a system facilitated by a mobile artefact to effectively identify crop disorder incidents and manage them using recommended control measures. This work overcomes the limitations of the existing attempts by using digital technology to empower farmers to identify crop disorders [...] Read more.
We have developed a system facilitated by a mobile artefact to effectively identify crop disorder incidents and manage them using recommended control measures. This work overcomes the limitations of the existing attempts by using digital technology to empower farmers to identify crop disorders rather than replace them with automated techniques. Our approach empowers farmers by providing the information in context for them to identify crop disorders. The developed solution can identify most of the crop disorders instantaneously, irrespective of the crop or other factors that make crop disorder identification complicated. For the rest, it provides a mechanism to carry out a manual identification with the help of subject experts. The solution was deployed among paddy farmers in Sri Lanka to understand how well this could assist them in identifying and managing crop disorders. The system was able to identify 70.8% of the crop disorder incidents reported by the farmers and provided them with the relevant control measures. Farmers’ perceptions of various usability aspects of the solution revealed that the application of agrochemicals and expenses associated with agrochemicals were significantly reduced. It was also observed that the yield quality and quantity and overall revenue have increased compared to the previous seasons. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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19 pages, 6989 KiB  
Article
A System for Optimizing the Process of Straw Bale Retrieval
by Mahdi Vahdanjoo, Michael Nørremark and Claus G. Sørensen
Sustainability 2021, 13(14), 7722; https://0-doi-org.brum.beds.ac.uk/10.3390/su13147722 - 10 Jul 2021
Cited by 2 | Viewed by 2065
Abstract
During a baling operation, the operator of the baler should decide when and where to drop the bales in the field to facilitate later retrieval of the bales for transport out of the field. Manually determining the time and place to drop a [...] Read more.
During a baling operation, the operator of the baler should decide when and where to drop the bales in the field to facilitate later retrieval of the bales for transport out of the field. Manually determining the time and place to drop a bale creates extra workload on the operator and may not result in the optimum drop location for the subsequent front loader and transport unit. Therefore, there is a need for a tool that can support operators during this decision process. The key objective of this study is to find the optimal traversal sequence of fieldwork tracks to be followed by the baler and bale retriever to minimize the non-working driving distance in the field. Two optimization processes are considered for this problem. Firstly, finding the optimal sequence of fieldwork tracks considering the constraints of the problem such as the capacity of the baler and the straw yield map of the field. Secondly, finding the optimal location and number of bales to drop in the field. A simulation model is developed to calculate all the non-productive traversal distances by baler and bale retrieval in the field. In a case study, the collected positional and temporal data from the baling process related to a sample field were considered. The output of the simulation model was compared with the conventional method applied by the operators. The results show that application of the proposed method can increase efficiency by 12.9% in comparison with the conventional method with edited data where the random movements (due to re-baling, turns in the middle of the swath, reversing, etc.) were removed from the data set. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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20 pages, 780 KiB  
Article
Smart Farming Technologies in Arable Farming: Towards a Holistic Assessment of Opportunities and Risks
by Sebastian Lieder and Christoph Schröter-Schlaack
Sustainability 2021, 13(12), 6783; https://0-doi-org.brum.beds.ac.uk/10.3390/su13126783 - 15 Jun 2021
Cited by 18 | Viewed by 5802
Abstract
Agricultural production finds itself in an area of tension. As a critical infrastructure, it has the task of reliably feeding a growing global population and supplying it with energy. However, the negative environmental impacts caused by agriculture, such as the global loss of [...] Read more.
Agricultural production finds itself in an area of tension. As a critical infrastructure, it has the task of reliably feeding a growing global population and supplying it with energy. However, the negative environmental impacts caused by agriculture, such as the global loss of biodiversity and the emission of greenhouse gases, are to be reduced. The increasing use of digital technologies is often described as a panacea that enables sustainable agriculture. The relevant literature is very dynamic, but the large number of concepts and terminologies used makes it difficult to obtain an overall view. In addition, many contributions focus on presumed or modeled efficiency gains, but this ignores technical and societal prerequisites and barriers. Therefore, the aim of this work was to identify the opportunities and risks of smart farming (SF) for more ecological arable farming. For this purpose, a holistic and environmental view was taken. The potential of SF to aid in the reduction in the environmental impacts of individual agricultural work steps was examined via an analysis of current literature. In addition, rebound effects, acceptance barriers and political omissions were considered as risks that prevent the benefits from being realized. It was shown that SF is able to contribute to a significant reduction in the negative environmental effects of agriculture. In particular, a reduction in fertilizer and pesticide application rates through mapping, sensing and precise application can lead to environmental benefits. However, achieving this requires the minimization of existing risks. For this reason, a proactive role of the state is required, implementing the necessary governance measures. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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24 pages, 1185 KiB  
Article
Towards an Understanding of the Behavioral Intentions and Actual Use of Smart Products among German Farmers
by Sirkka Schukat and Heinke Heise
Sustainability 2021, 13(12), 6666; https://0-doi-org.brum.beds.ac.uk/10.3390/su13126666 - 11 Jun 2021
Cited by 13 | Viewed by 4061
Abstract
Innovative technologies in the context of smart farming are expected to play a significant role in the adaptation of the agricultural sector to climate change and sustainable agriculture. However, the adoption of smart farming solutions, in this case so-called smart products, depends indispensably [...] Read more.
Innovative technologies in the context of smart farming are expected to play a significant role in the adaptation of the agricultural sector to climate change and sustainable agriculture. However, the adoption of smart farming solutions, in this case so-called smart products, depends indispensably on the acceptance of farmers. For this reason, it is important to develop an understanding of what determinants are decisive for farmers in the adoption of these technologies. In order to address this research gap, farmers in Germany were surveyed via a large-scale online survey in 2020 (n = 523). Based on an extended version of the Unified Theory of Acceptance and Use of Technology, a Partial Least Squares (PLS) analysis was performed. The results indicate that hedonic motivation significantly influences farmers’ behavioral intention to use smart products. In addition, behavioral intention is affected by social determinants and the personal performance expectations of smart products. Trust, as well as facilitating conditions, also has an impact on behavioral intention. Furthermore, facilitating conditions are an important determinant of the actual use behavior. In addition, use behavior is influenced by behavioral intention. It was further found that technology readiness plays a significant role in the adoption of smart products. Moderating effects of age, work experience, and farm size were identified that influence farmers’ willingness to use smart products. The study holds important managerial implications for technology companies in the field of smart farming and can help develop approaches for tailored technical solutions that meet farmers’ needs. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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19 pages, 511 KiB  
Article
An Analytical Framework to Study Multi-Actor Partnerships Engaged in Interactive Innovation Processes in the Agriculture, Forestry, and Rural Development Sector
by Evelien Cronin, Sylvie Fosselle, Elke Rogge and Robert Home
Sustainability 2021, 13(11), 6428; https://0-doi-org.brum.beds.ac.uk/10.3390/su13116428 - 05 Jun 2021
Cited by 10 | Viewed by 2882
Abstract
Communities of practice (CoPs) interact with a range of external stakeholders who collectively influence the direction of the community and the achievement of its goals. In the case of multi-actor co-innovation partnerships, which are perceived as a type of combination between a community [...] Read more.
Communities of practice (CoPs) interact with a range of external stakeholders who collectively influence the direction of the community and the achievement of its goals. In the case of multi-actor co-innovation partnerships, which are perceived as a type of combination between a community of practice and innovation network in this paper, internal and external interactions consequently influence the ability of these partnerships to co-innovate. The aim of this contribution is to develop an analytical framework to understand the factors and processes that enable or hinder interactions, both within and external to multi-actor co-innovation partnerships. The analytical framework was built around interactions with funding mechanisms, external stakeholders, the context/environment, and societal challenges, along with interactions within the partnership. Each of these five interactions is influenced by structures and capacity, along with how these combine to overcome the challenges faced by the partnership. For this study, 30 case study multi-actor co-innovation partnerships from across Europe were selected and analysed according to the framework. The results show that interactions with funding bodies can lead to partnerships adapting to what they perceive to be the goals of the funding body, and sometimes to the overpromising of expected outputs in an effort to win scarce funding. The reflection of societal needs in the goals of funding bodies could thereby capitalize on the motivations and aspirations of partnerships to combine socio-economic and environmental benefits at both individual and societal levels. Factors that enable partnerships to achieve their own goals are commonly based around the inclusion or recruitment of experienced partners with existing networks, in which the partnership may be embedded, that can facilitate internal collaboration and navigate the external environments, such as political structures and market conditions. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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14 pages, 11963 KiB  
Article
Development of an Online Tool for Tracking Soil Nitrogen to Improve the Environmental Performance of Maize Production
by Giovani Preza-Fontes, Junming Wang, Muhammad Umar, Meilan Qi, Kamaljit Banger, Cameron Pittelkow and Emerson Nafziger
Sustainability 2021, 13(10), 5649; https://0-doi-org.brum.beds.ac.uk/10.3390/su13105649 - 18 May 2021
Cited by 2 | Viewed by 2006
Abstract
Freshwater nitrogen (N) pollution is a significant sustainability concern in agriculture. In the U.S. Midwest, large precipitation events during winter and spring are a major driver of N losses. Uncertainty about the fate of applied N early in the growing season can prompt [...] Read more.
Freshwater nitrogen (N) pollution is a significant sustainability concern in agriculture. In the U.S. Midwest, large precipitation events during winter and spring are a major driver of N losses. Uncertainty about the fate of applied N early in the growing season can prompt farmers to make additional N applications, increasing the risk of environmental N losses. New tools are needed to provide real-time estimates of soil inorganic N status for corn (Zea mays L.) production, especially considering projected increases in precipitation and N losses due to climate change. In this study, we describe the initial stages of developing an online tool for tracking soil N, which included, (i) implementing a network of field trials to monitor changes in soil N concentration during the winter and early growing season, (ii) calibrating and validating a process-based model for soil and crop N cycling, and (iii) developing a user-friendly and publicly available online decision support tool that could potentially assist N fertilizer management. The online tool can estimate real-time soil N availability by simulating corn growth, crop N uptake, soil organic matter mineralization, and N losses from assimilated soil data (from USDA gSSURGO soil database), hourly weather data (from National Weather Service Real-Time Mesoscale Analysis), and user-entered crop management information that is readily available for farmers. The assimilated data have a resolution of 2.5 km. Given limitations in prediction accuracy, however, we acknowledge that further work is needed to improve model performance, which is also critical for enabling adoption by potential users, such as agricultural producers, fertilizer industry, and researchers. We discuss the strengths and limitations of attempting to provide rapid and cost-effective estimates of soil N availability to support in-season N management decisions, specifically related to the need for supplemental N application. If barriers to adoption are overcome to facilitate broader use by farmers, such tools could balance the need for ensuring sufficient soil N supply while decreasing the risk of N losses, and helping increase N use efficiency, reduce pollution, and increase profits. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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16 pages, 852 KiB  
Article
The Digitalization of Agriculture and Rural Areas: Towards a Taxonomy of the Impacts
by Silvia Rolandi, Gianluca Brunori, Manlio Bacco and Ivano Scotti
Sustainability 2021, 13(9), 5172; https://0-doi-org.brum.beds.ac.uk/10.3390/su13095172 - 06 May 2021
Cited by 51 | Viewed by 9451
Abstract
The literature about digitalization in agriculture and rural areas is vast and sectorial at the same time. Both international political institutions and practitioners are interested in promoting digital technology, indicating and describing potential benefits and risks. Meanwhile, academics analyze the actual and possible [...] Read more.
The literature about digitalization in agriculture and rural areas is vast and sectorial at the same time. Both international political institutions and practitioners are interested in promoting digital technology, indicating and describing potential benefits and risks. Meanwhile, academics analyze the actual and possible impacts of digital technologies by using case studies. However, the extensive literature makes it challenging to derive a comprehensive synthesis of the possible impacts that digital technologies are and might generate in the rural domains. In the given context, the present work aims at contributing to the construction of a framework providing a first classification of the digital technologies’ impacts to use in both research and a political agenda. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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12 pages, 851 KiB  
Article
A Path Model of the Intention to Adopt Variable Rate Irrigation in Northeast Italy
by Maurizio Canavari, Marco Medici, Rungsaran Wongprawmas, Vilma Xhakollari and Silvia Russo
Sustainability 2021, 13(4), 1879; https://0-doi-org.brum.beds.ac.uk/10.3390/su13041879 - 09 Feb 2021
Cited by 5 | Viewed by 3138
Abstract
Irrigated agriculture determines large blue water withdrawals, and it is considered a key intervention area to reach sustainable development objectives. Precision agriculture technologies have the potential to mitigate water resource depletion that often characterises conventional agricultural approaches. This study investigates the factors influencing [...] Read more.
Irrigated agriculture determines large blue water withdrawals, and it is considered a key intervention area to reach sustainable development objectives. Precision agriculture technologies have the potential to mitigate water resource depletion that often characterises conventional agricultural approaches. This study investigates the factors influencing farmers’ intentions to adopt variable rate irrigation (VRI) technology. The Technology Acceptance Model 3 (TAM-3) was employed as a theoretical framework to design a survey to identify the factors influencing farmers’ decision-making process when adopting VRI. Data were gathered through quantitative face-to-face interviews with a sample of 138 fruit and grapevine producers from the Northeast of Italy (Veneto, Emilia-Romagna, Trentino-Alto Adige, Friuli-Venezia Giulia). Data were analysed using partial least squares path modelling (PLS-PM). The results highlight that personal attitudes, such as perceived usefulness and subjective norm, positively influence the intention to adopt VRI. Additionally, the perceived ease of use positively affects intention, but it is moderated by subject experience. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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19 pages, 4037 KiB  
Article
Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt
by Mohamed K. Abdel-Fattah, Elsayed Said Mohamed, Enas M. Wagdi, Sahar A. Shahin, Ali A. Aldosari, Rosa Lasaponara and Manal A. Alnaimy
Sustainability 2021, 13(4), 1824; https://0-doi-org.brum.beds.ac.uk/10.3390/su13041824 - 08 Feb 2021
Cited by 37 | Viewed by 4925
Abstract
Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert [...] Read more.
Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights (Wi) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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17 pages, 3402 KiB  
Article
Potential of On-the-Go Gamma-Ray Spectrometry for Estimation and Management of Soil Potassium Site Specifically
by Anuar Mohamed Kassim, Said Nawar and Abdul M. Mouazen
Sustainability 2021, 13(2), 661; https://0-doi-org.brum.beds.ac.uk/10.3390/su13020661 - 12 Jan 2021
Cited by 13 | Viewed by 2350
Abstract
High resolution data on plant available potassium (Ka) is crucial to optimize variable rate potassium fertilizer recommendations, and subsequently improve crop growth and yield. A gamma-ray passive spectrometry sensor was evaluated for on-the-go mapping and management of the spatial distribution of Ka over [...] Read more.
High resolution data on plant available potassium (Ka) is crucial to optimize variable rate potassium fertilizer recommendations, and subsequently improve crop growth and yield. A gamma-ray passive spectrometry sensor was evaluated for on-the-go mapping and management of the spatial distribution of Ka over a 8.4 ha field at Huldenberg, Belgium. During the on-the-go measurement, a 5 s sampling interval was used while driving at 3 km/h speed along 10 m parallel transects. Two calibration models to predict Ka across the field were developed and compared: (1) a simple third order polynomial function (3DPF) was established between the sensor reading of the naturally occurring radioactive isotope of potassium (K-40) and laboratory measured Ka and (2) a partial least squares regression (PLSR) model linking gamma-ray spectra and laboratory measured Ka. Although a relatively small number of samples (45 samples) were used for the development of the PLSR calibration model, the cross-validation analysis resulted in a very good performance with a coefficient of determination (R2) of 0.85, a residual prediction deviation (RPD) of 2.67, a root mean square error of cross-validation (RMSECV) of 2.29 (mg/100 g) and a ratio of performance to interquartile distance (RPIQ) of 2.61. This was a much better result that that obtained with the 3DPF model (R2 = 0.69). The spatial distribution of Ka developed based on 3DPF and PLSR methods showed great similarity with the corresponding map developed using the data from the laboratory analysis. The calculated variable rate fertilizer recommendation based on gamma-ray data showed marginal differences in the amount of K2O fertilizer applied, compared to the uniform rate fertilization based on the conventional laboratory chemical soil analyses. The on-the-go measurement of Ka using gamma-ray spectrometry shows high potential, although the technology needs to be evaluated in a larger number of fields. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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24 pages, 1795 KiB  
Article
Integrated Sustainability Assessment of Divergent Mediterranean Farming Systems: Cyprus as a Case Study
by Andreas Stylianou, Despina Sdrali and Constantinos D. Apostolopoulos
Sustainability 2020, 12(15), 6105; https://0-doi-org.brum.beds.ac.uk/10.3390/su12156105 - 29 Jul 2020
Cited by 10 | Viewed by 4252
Abstract
A variety of indicator-based methods have been developed for the sustainability assessment of farming systems (FSs). However, many of them lack holisticity, focus on a specific agricultural sector/product, and do not provide aggregated results to better support decision-making process. The goal of this [...] Read more.
A variety of indicator-based methods have been developed for the sustainability assessment of farming systems (FSs). However, many of them lack holisticity, focus on a specific agricultural sector/product, and do not provide aggregated results to better support decision-making process. The goal of this study was, for the first time, to assess, in a holistic manner, the sustainability performance of different FSs in southeastern Cyprus. The methodological framework involved three major steps. First, the sustainability context was set, and a list of 41 environmental, social, and economic indicators was created. The indicators were then calculated using data from 324 farms. Second, six FSs were identified using multivariate analysis. Finally, the sustainability of FSs was assessed by combining numerical (construction of four composite sustainability indices) and visual (presentation of indicator scores and values with graphs and tables) integration approaches. While the indices provided the “big picture”, visual integration revealed the areas where policy interventions are needed. The analysis showed that sustainable agricultural practices are already used by some farmers in the area. The results could be used for benchmarking purposes and to aid decision-making process in Cyprus but might also be useful for other Mediterranean regions with similar agro-ecological conditions. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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9 pages, 664 KiB  
Article
A Meta-Analytic Approach to Predict Methane Emissions from Dairy Goats Using Milk Fatty Acid Profile
by Francisco Requena, Francisco Peña, Estrella Agüera and Andrés Martínez Marín
Sustainability 2020, 12(12), 4834; https://0-doi-org.brum.beds.ac.uk/10.3390/su12124834 - 13 Jun 2020
Cited by 7 | Viewed by 2375
Abstract
The aim of this work was to develop an equation to predict methane yield (CH4, g/kg dry matter intake) from dairy goats using milk fatty acid (FA) profile. Data from 12 research papers (30 treatments and 223 individual observations) were used [...] Read more.
The aim of this work was to develop an equation to predict methane yield (CH4, g/kg dry matter intake) from dairy goats using milk fatty acid (FA) profile. Data from 12 research papers (30 treatments and 223 individual observations) were used in a meta-regression. Since most of the selected studies did not extensively report milk fat composition, palmitic acid (C16:0) was selected as a potential predictor. The obtained equation was: CH4 (g/kg dry matter intake) = 0.525 × C16:0 (% in milk fat). The coefficient of determination (R2 = 0.46), the root mean square error of prediction (RMSPE = 3.16 g/kg dry matter intake), and the concordance correlation coefficient (CCC = 0.65) indicated that the precision, accuracy and reproducibility of the model were moderate. The relationship between CH4 yield and C16:0 content in milk fat would be supported by the fact that diet characteristics that increase the amount of available hydrogen in the rumen for archaea to produce CH4, simultaneously favor the conditions for the synthesis of C16:0 in the mammary gland. The obtained equation might be useful, along with previous published equations based on diet characteristics, to evaluate the environmental impact of dairy goat farming. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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18 pages, 2033 KiB  
Article
Efficiency and Sustainability in Farm Diversification Initiatives in Northern Spain
by Beatriz García-Cornejo, José A. Pérez-Méndez, David Roibás and Alan Wall
Sustainability 2020, 12(10), 3983; https://0-doi-org.brum.beds.ac.uk/10.3390/su12103983 - 13 May 2020
Cited by 21 | Viewed by 3172
Abstract
The value-added diversification strategy provides an option for guaranteeing the sustainability of small farms. This study examines how factors related to managerial strategy and socio-environmental sustainability influence the efficiency of diversification initiatives. For this purpose, we use a novel and unique database of [...] Read more.
The value-added diversification strategy provides an option for guaranteeing the sustainability of small farms. This study examines how factors related to managerial strategy and socio-environmental sustainability influence the efficiency of diversification initiatives. For this purpose, we use a novel and unique database of value-added ventures implemented by 49 dairy farms located in northern Spain. We construct a production frontier using a Data Envelopment Analysis (DEA) model to estimate technical efficiency. The mean technical efficiency of the initiatives was 0.56 and 0.59 for the constant and variable returns specifications, respectively. Determinants of efficiency are analyzed with a two-step procedure with a double bootstrap. We find that the elaborations of more complex products other than fresh milk are negatively associated with efficiency. However, specialization in one product with different variants and direct sales both have a positive association with efficiency. In terms of socio-environmental variables, there is a positive association between efficiency and the use of quality schemes such as ‘protected designation of origin’ (PDO), the use of organic labelling and the farmer having university education, and a negative association with the percentage of family labor. Our findings support the idea that value-added diversification contributes to more resilient pathways of development and underlines the importance of good quality management of marketing and operational factors. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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Review

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18 pages, 1306 KiB  
Review
Patterns of Inequalities in Digital Agriculture: A Systematic Literature Review
by Sarah Hackfort
Sustainability 2021, 13(22), 12345; https://0-doi-org.brum.beds.ac.uk/10.3390/su132212345 - 09 Nov 2021
Cited by 45 | Viewed by 6273
Abstract
Digitalization of agriculture is often hailed as the next agricultural revolution. However, little is yet known about its social impacts and power effects. This review addresses this research gap by analyzing patterns of inequality linked to the development and adoption of digital technologies [...] Read more.
Digitalization of agriculture is often hailed as the next agricultural revolution. However, little is yet known about its social impacts and power effects. This review addresses this research gap by analyzing patterns of inequality linked to the development and adoption of digital technologies in agriculture and reviewing the strategies developed to reduce these inequalities and challenge the power relations in which they are embedded. Analysis of 84 publications found through a systematic literature review identified five patterns of inequality: (1) in digital technology development; (2) in the distribution of benefits from the use of digital technologies; (3) in sovereignty over data, hardware and digital infrastructure; (4) in skills and knowledge (‘digital literacy’); and (5) in problem definition and problem-solving capacities. This review also highlights the existence of emancipatory initiatives that are applying digital technologies to challenge existing inequalities and to advance alternative visions of agriculture. These initiatives underscore the political nature of digital agriculture; however, their reach is still quite limited. This is partly due to the fact that existing inequalities are structural and represent expressions of corporate power. From such a perspective, digitalization in agriculture is not a ‘revolution’ per se; rather, digital technologies mirror and reproduce existing power relations. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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31 pages, 2451 KiB  
Review
Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture
by Nawab Khan, Ram L. Ray, Ghulam Raza Sargani, Muhammad Ihtisham, Muhammad Khayyam and Sohaib Ismail
Sustainability 2021, 13(9), 4883; https://0-doi-org.brum.beds.ac.uk/10.3390/su13094883 - 27 Apr 2021
Cited by 89 | Viewed by 29934
Abstract
The agricultural industry is getting more data-centric and requires precise, more advanced data and technologies than before, despite being familiar with agricultural processes. The agriculture industry is being advanced by various information and advanced communication technologies, such as the Internet of Things (IoT). [...] Read more.
The agricultural industry is getting more data-centric and requires precise, more advanced data and technologies than before, despite being familiar with agricultural processes. The agriculture industry is being advanced by various information and advanced communication technologies, such as the Internet of Things (IoT). The rapid emergence of these advanced technologies has restructured almost all other industries, as well as advanced agriculture, which has shifted the industry from a statistical approach to a quantitative one. This radical change has shaken existing farming techniques and produced the latest prospects in a series of challenges. This comprehensive review article enlightens the potential of the IoT in the advancement of agriculture and the challenges faced when combining these advanced technologies with conventional agricultural systems. A brief analysis of these advanced technologies with sensors is presented in advanced agricultural applications. Numerous sensors that can be implemented for specific agricultural practices require best management practices (e.g., land preparation, irrigation systems, insect, and disease management). This review includes the integration of all suitable techniques, from sowing to harvesting, packaging, transportation, and advanced technologies available for farmers throughout the cropping system. Besides, this review article highlights the utilization of other tools such as unmanned aerial vehicles (UAVs) for crop monitoring and other beneficiary measures, such as optimizing crop yields. In addition, advanced programs based on the IoT are also discussed. Finally, based on our comprehensive review, we identified advanced prospects regarding the IoT, which are essential tools for sustainable agriculture. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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Technical Note
AgroTutor: A Mobile Phone Application Supporting Sustainable Agricultural Intensification
by Juan Carlos Laso Bayas, Andrea Gardeazabal, Mathias Karner, Christian Folberth, Luis Vargas, Rastislav Skalský, Juraj Balkovič, Anto Subash, Moemen Saad, Sylvain Delerce, Jesús Crespo Cuaresma, Jaroslava Hlouskova, Janet Molina-Maturano, Linda See, Steffen Fritz, Michael Obersteiner and Bram Govaerts
Sustainability 2020, 12(22), 9309; https://0-doi-org.brum.beds.ac.uk/10.3390/su12229309 - 10 Nov 2020
Cited by 8 | Viewed by 3428
Abstract
Traditional agricultural extension services rely on extension workers, especially in countries with large agricultural areas. In order to increase adoption of sustainable agriculture, the recommendations given by such services must be adapted to local conditions and be provided in a timely manner. The [...] Read more.
Traditional agricultural extension services rely on extension workers, especially in countries with large agricultural areas. In order to increase adoption of sustainable agriculture, the recommendations given by such services must be adapted to local conditions and be provided in a timely manner. The AgroTutor mobile application was built to provide highly specific and timely agricultural recommendations to farmers across Mexico and complement the work of extension agents. At the same time, AgroTutor provides direct contributions to the United Nations Sustainable Development Goals, either by advancing their implementation or providing local data systems to measure and monitor specific indicators such as the proportion of agricultural area under productive and sustainable agriculture. The application is freely available and allows farmers to geo-locate and register plots and the crops grown there, using the phone’s built-in GPS, or alternatively, on top of very high-resolution imagery. Once a crop and some basic data such as planting date and cultivar type have been registered, the application provides targeted information such as weather, potential and historical yield, financial benchmarking information, data-driven recommendations, and commodity price forecasts. Farmers are also encouraged to contribute in-situ information, e.g., soils, management, and yield data. The information can then be used by crop models, which, in turn, send tailored results back to the farmers. Initial feedback from farmers and extension agents has already improved some of the application’s characteristics. More enhancements are planned for inclusion in the future to increase the application’s function as a decision support tool. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
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