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Digital Agriculture for Sustainable Food Systems: Implications for Land-Resource Use and Management

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A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 27372

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Special Issue Editors

Dr. Assem Abu Hatab

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Guest Editor

Department of Economics, Swedish University of Agricultural Sciences, 754 26 Uppsala, Sweden
Interests: agricultural economics; agricultural systems; urbanization; food and agricultural policy; food value chains; food security; developing countries; Egypt; China

Dr. Nasem Baderldin

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Guest Editor

Digital Agronomy, Department of Soil Science, Faculty of Agricultural and Food Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
Interests: remote sensing and geospatial data modeling; machine learning; landscape conservation; soil erosion

Dr. Zhen Liu

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Guest Editor

School of Business, Nanjing Normal University, Nanjing 210023, China
Interests: environmental and resource economics; development economics

Special Issue Information

Dear Colleagues,

Today's food systems face manifold sustainability challenges. Outcomes of these food systems contribute to poverty, social and income inequalities, as well as the triple burden of malnutrition. In addition, current food systems are environmentally unsustainable, as they are associated with greenhouse gas emissions, biodiversity loss, water and air pollution and soil degradation, and characterized by their low resistance to natural and socioeconomic shocks. By 2050, the global food system must feed nine billion people, out of which around eight billion would be in developing countries. Feeding such populations sustainably and more fairly not only requires increasing food availability but also addressing several substantial challenges (e.g., climate change) and ensuring that food is produced in ways that allow for its sustained supply, do not degrade our ability to produce food in the future, and do not seriously compromise critically important ecosystem services.

In this context, the incremental developments in agricultural technology have empowered research and management capabilities to integrate more effectively with real-world challenges. Digital agriculture as a new research era brings together the scientific community, governments, stakeholders, and the public to discover the potential of this endeavor to facilitate their goals and needs. In addition, this technology has a promising outcome for the rural agricultural developments as it provides the accessibility to invaluable datasets on climate, seeds, fertilizers, harvest, and markets.

Satellites, airborne sensors, unmanned aerial vehicles (UAVs), and wireless sensors networks (WSNs) are now available tools in our world that provide information and help to make decisions. The Internet of Things (IoT) helps clarify this complex interaction between several parties, which encourage proactivity to react quickly, more effectively, and with less cost. Big data in our modern world is the greatest asset, as it shows information in high space and time resolution scales, builds robust machine learning algorithms, and produces informative layouts. Therefore, reliable agricultural and environmental policies can be produced, more social awareness and engagements set up, and lucrative farming strategies and healthier ecosystems developed.

The purpose of this Special Issue is to provide a platform to enhance interdisciplinary research and share the most recent ideas and novel ways in monitoring, assessing, modeling, and predicting land cover/land use dynamics, land health, yield using remote sensing technologies and artificial intelligence. The ultimate goal is to propose and deliver solutions and information that can be applied to various several agricultural and environmental contexts to build more sustainable agricultural systems that adapt to environmental changes, foster food security, and meet the needs of the increasing population. The targeted audience includes both academic researchers and practitioners. In particular, published articles would cover but are not limited to the following topics:

Digital agriculture and agricultural systems' resilience and sustainability
Digital agronomy applications for improving land health, environmental conservation and restoration, and food security
Digital agriculture and natural-resource protection and restoration
Approaches to assess land cover alter or/and predict land use productivity
Technological innovation, digital agronomy and agricultural and environmental policies
The use of spatiotemporal data in land conservation and climate change impacts
The use of artificial intelligence in land cover and land use modeling
The interaction between environment, farms, and markets through IoT-based smart farming

Dr. Assem Abu Hatab
Dr. Nasem Baderldin
Dr. Zhen Liu
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. Land is an international peer-reviewed open access monthly 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 2600 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

landcover dynamics
big data
Artificial Intelligence (AI)
digital farming
remote sensing
spatiotemporal analysis
IoT-Based agriculture, conservation policies
agriculture systems
sustainable development

Published Papers (9 papers)

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Research

19 pages, 2278 KiB

Open AccessArticle

Toward Cleaner Production: Can Mobile Phone Technology Help Reduce Inorganic Fertilizer Application? Evidence Using a National Level Dataset

by Nawab Khan, Ram L. Ray, Hazem S. Kassem, Muhammad Ihtisham, Abdullah, Simplice A. Asongu, Stephen Ansah and Shemei Zhang

Land 2021, 10(10), 1023; https://doi.org/10.3390/land10101023 - 29 Sep 2021

Cited by 18 | Viewed by 2392

Abstract

Increasing agricultural production and optimizing inorganic fertilizer (IF) use are imperative for agricultural and environmental sustainability. Mobile phone usage (MPU) has the potential to reduce IF application while ensuring environmental and agricultural sustainability goals. The main objectives of this study were to assess MPU, mobile phone promotion policy, and whether the mediation role of human capital can help reduce IF use. This study used baseline regression analysis and propensity score matching, difference-in-differences (PSM-DID) to assess the impact of MPU on IF usage. However, the two-stage instrumental variables method (IVM) was used to study the effects of mobile phone promotion policy on IF usage. This study used a national dataset from 7987 rural households in Afghanistan to investigate the impacts of MPU and associated promotion policies on IF application. The baseline regression outcomes showed that the MPU significantly reduced IF usage. The evaluation mechanism revealed that mobile phones help reduce IF application by improving the human capital of farmers. Besides, evidence from the DID technique showed that mobile phone promotion policies lowered IF application. These results remained robust after applying the PSM-DID method and two-stage IVM to control endogenous decisions of rural households. This study results imply that enhancing the accessibility of wideband in remote areas, promoting MPU, and increasing investment in information communication technologies (ICTs) infrastructure can help decrease the IF application in agriculture. Thus, the government should invest in remote areas to facilitate access to ICTs, such as having a telephone and access to a cellular and internet network to provide an environment and facility to apply IF effectively. Further, particular policy support must focus on how vulnerable populations access the internet and mobile phone technologies. Full article

(This article belongs to the Special Issue Digital Agriculture for Sustainable Food Systems: Implications for Land-Resource Use and Management)

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22 pages, 482 KiB

Open AccessArticle

New Round of Collective Forest Rights Reform, Forestland Transfer and Household Production Efficiency

by Jinna Yu, Yiming Wei, Wei Fang, Zhen Liu, Yujie Zhang and Jing Lan

Land 2021, 10(9), 988; https://doi.org/10.3390/land10090988 - 18 Sep 2021

Cited by 10 | Viewed by 1652

Abstract

The purpose of this paper was to analyze the influence mechanism of the new round of Collective Forest Rights Reform (CFRR) on farmers’ production efficiency from the perspective of forestland transfer. Based on the panel data of field investigation in Jiangxi Province, a panel logit model was used to verify whether the new round of CFRR has affected farmers’ forestland circulation behavior. The results showed that the new round of CFRR has played a significant role in promoting forestland circulation. Secondly, the non-parametric DEA method was used to estimate the technical, scale, and comprehensive efficiency of households. DID and panel quantile models were constructed to analyze the impact of forestland inflow policy and forestland outflow policy effects on rural household productivity. The regression results showed that the effect of forestland inflow has had a significantly positive impact on scale and comprehensive efficiency, but it only had a significant effect on technical efficiency in the 0.1 quartile. The effect of forestland outflow was not found to be significant for technical, scale, and comprehensive efficiency, but it was found to be negative for technical efficiency in the 0.75 quartile and negative for scale efficiency in the 0.5 and 0.75 quantiles. Full article

(This article belongs to the Special Issue Digital Agriculture for Sustainable Food Systems: Implications for Land-Resource Use and Management)

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17 pages, 878 KiB

Open AccessArticle

How Does Tie Strength Dispersion within Inter-Organizational Networks Affect Agricultural Technological Innovation? Evidence from China

by Long Cheng, Shiyuan Zhang, Xuming Lou, Jie Huang, Fangping Rao and Rui Bai

Land 2021, 10(7), 717; https://doi.org/10.3390/land10070717 - 07 Jul 2021

Cited by 2 | Viewed by 1712

Abstract

Agricultural technology is key to ensuring food security. Innovation in agricultural technology plays a vital role in increasing national food production. Collaborative innovation has become an essential form of technological innovation in the new era. Although there has been a large body of literature exploring the influencing factors on technological innovation, how tie strength dispersion within inter-organizational networks affects agricultural technological innovation has not been systematically studied. In this research, we use a cooperative network to investigate how relational divisive faultlines caused by the uneven distribution of the strength of inter-organizational relationships affects agricultural technological innovation through the subgroup structure, and the moderating role of position embeddedness. This article uses the Derwent Innovations Index to select agricultural technology joint patent applications from 2000 to 2018 to build a cooperation network, and uses multiple linear regression to conduct an empirical analysis. The empirical results show that the relational divisive faultlines have a positive effect on the subgroup structure. There is an inverted U-shaped relationship between the subgroup structure and agricultural technological innovation. The initial stage of subgroup formation can transmit the information between the subgroups in time and promote the efficiency of agricultural technological innovation. However, as the degree of subgroup cohesion increases, the phenomenon of “in-group” and “out-of-group” will be formed, which will inhibit information exchange, having a negative impact on agricultural technological innovation. In addition, positional embeddedness has a significant positive moderating effect between relational divisive faultlines and agricultural technological innovation. This research provides a theoretical basis for understanding how the overall network relationship strength distribution affects technological innovation by exploring the micro-process of the structural changes of the cooperation network. Moreover, it has specific guiding significance for the organization to participation in a cooperation network to improve the efficiency of agricultural technological innovation. Full article

(This article belongs to the Special Issue Digital Agriculture for Sustainable Food Systems: Implications for Land-Resource Use and Management)

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20 pages, 4592 KiB

Open AccessArticle

A Two-Stage Approach to the Estimation of High-Resolution Soil Organic Carbon Storage with Good Extension Capability

by Sunwei Wei, Zhengyong Zhao, Qi Yang and Xiaogang Ding

Land 2021, 10(5), 517; https://doi.org/10.3390/land10050517 - 13 May 2021

Cited by 1 | Viewed by 1715

Abstract

Soil organic carbon storage (SOCS) estimation is a crucial branch of the atmospheric–vegetation–soil carbon cycle study under the background of global climate change. SOCS research has increased worldwide. The objective of this study is to develop a two-stage approach with good extension capability to estimate SOCS. In the first stage, an artificial neural network (ANN) model is adopted to estimate SOCS based on 255 soil samples with five soil layers (20 cm increments to 100 cm) in Luoding, Guangdong Province, China. This method is compared with three common methods: The soil type method (STM), ordinary kriging (OK), and radial basis function (RBF) interpolation. In the second stage, a linear model is introduced to capture the regional differences and further improve the estimation accuracy of the Luoding-based ANN model when extending it to Xinxing, Guangdong Province. This is done after assessing the generalizability of the above four methods with 120 soil samples from Xinxing. The results for the first stage show that the ANN model has much better estimation accuracy than STM, OK, and RBF, with the average root mean square error (RMSE) of the five soil layers decreasing by 0.62–0.90 kg·m⁻², R² increasing from 0.54 to 0.65, and the mean absolute error decreasing from 0.32 to 0.42. Moreover, the spatial distribution maps produced by the ANN model are more accurate than those of other methods for describing the overall and local SOCS in detail. The results of the second stage indicate that STM, OK, and RBF have poor generalizability (R² < 0.1), and the R² value obtained with ANN method is also 43–56% lower for the five soil layers compared with the estimation accuracy achieved in Luoding. However, the R² of the linear models built with the 20% soil samples from Xinxing are 0.23–0.29 higher for the five soil layers. Thus, the ANN model is an effective method for accurately estimating SOCS on a regional scale with a small number of field samples. The linear model could easily extend the ANN model to outside areas where the ANN model was originally developed with a better level of accuracy. Full article

(This article belongs to the Special Issue Digital Agriculture for Sustainable Food Systems: Implications for Land-Resource Use and Management)

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12 pages, 9505 KiB

Open AccessArticle

Model Prediction of the Soil Moisture Regime and Soil Nutrient Regime Based on DEM-Derived Topo-Hydrologic Variables for Mapping Ecosites

by Zhengyong Zhao, Qi Yang, Xiaogang Ding and Zisheng Xing

Land 2021, 10(5), 449; https://doi.org/10.3390/land10050449 - 23 Apr 2021

Cited by 3 | Viewed by 1677

Abstract

Ecosites are required for stand-level forest management and can be determined within a two-dimensional edatopic grid with soil nutrient regimes (SNRs) and soil moisture regimes (SMRs) as coordinates. A new modeling method is introduced in this study to map high-resolution SNR and SMR and then to design ecosites in Nova Scotia, Canada. Using coarse-resolution soil maps and nine topo-hydrologic variables derived from high-resolution digital elevation model (DEM) data as model inputs, 511 artificial neural network (ANN) models were developed by a 10-fold cross-validation with 1507 field samples to estimate 10 m resolution SNR and SMR maps. The results showed that the optimal models for mapping SNR and SMR engaged eight and seven topo-hydrologic variables, together with three coarse-resolution soil maps, as model inputs, respectively; 82% of model-estimated SNRs were identical to field assessments, while this value was 61% for SMRs, and the produced ecosite maps had 67–68% correctness. According to the error matrix, the predicted SNR and SMR maps greatly alleviated poor prediction in the areas of extreme nutrient or moisture conditions (e.g., very poor or very rich, wet, or very dry). Thus, the new method for modeling high-resolution SNR and SMR could be used to produce ecosite maps in sites where accessibility is hard. Full article

(This article belongs to the Special Issue Digital Agriculture for Sustainable Food Systems: Implications for Land-Resource Use and Management)

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15 pages, 712 KiB

Open AccessArticle

Effect of Land Property Rights on Forest Resources in Southern China

by Yang Yang, Hua Li, Long Cheng and Youliang Ning

Land 2021, 10(4), 392; https://doi.org/10.3390/land10040392 - 09 Apr 2021

Cited by 11 | Viewed by 1964

Abstract

The land tenure reform is important for forest resource management worldwide. Since China initiated a new round of collective forestland tenure reform (CFTR) in 2003, improving forest output by clarifying property rights plays a crucial role in realizing sustainable forest resource management. Using survey data of 312 bamboo plantation households from Southern China, this paper empirically examines the impact path of land property rights on forest resources. The estimation results show that both the forestland use right and disposal right are able to significantly improve the forest output by encouraging households to invest and optimizing the allocation of forestry labor. Particularly, the results reveal that the use right has a positive impact on forest output through forestland investment. With regard to the disposal right, we find that it has a positive effect on forest output through forestland investment, but it has a negative impact on forest output through the forestry labor allocation. The findings of this study suggest that to promote the growth of forest resources, the government should endow households with a more complete set of rights, and further strengthen their understanding of property rights. In addition, our findings enhance the understanding of the collective forestland tenure reform in China; they also have implications for the decentralized management of forestry elsewhere in the world. Full article

(This article belongs to the Special Issue Digital Agriculture for Sustainable Food Systems: Implications for Land-Resource Use and Management)

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18 pages, 542 KiB

Open AccessArticle

Does Internet and Information Technology Help Farmers to Maximize Profit: A Cross-Sectional Study of Apple Farmers in Shandong, China

by Fuhong Zhang, Apurbo Sarkar and Hongyu Wang

Land 2021, 10(4), 390; https://doi.org/10.3390/land10040390 - 08 Apr 2021

Cited by 16 | Viewed by 2881

Abstract

The adoption of Internet and Information Technology (IIT) in organizations has been growing at a staggering pace. In agriculture, IIT has emerged from the prospects of modern agricultural structure, which profoundly bring revolution in the way of agribusiness. While the impacts of IIT for selecting productive sales and marketing channels is evidenced by the substantial literature in the field, there is a crucial research scope of inclusive analytical views, especially in an economics context. The prime objective of the article is to assess the impacts of IIT for choosing a productive sales and marketing channel. Moreover, we tend to find whether the usage of IIT can eventually foster the profitability of the farmers. The empirical set of data is collected from a cross-sectional survey conducted in Shandong province, China. We utilize the Ordinary Least-Squares (OLS) regression, propensity score matching (PSM), and Heckman’s two-stage regression approaches to craft the findings. The greater extent of the use of IIT, the more significant and positive the impact of agricultural income is. After using the Heckman regression and PSM model, IIT’s use significantly increases the efficiency for selecting the sales channel, and the impact on agricultural income is also prominent (around 40%). We also find that the supporting and nonagricultural income exceeded 30%. Finally, the outcomes of the study reveal significant positive impacts for selecting productive sales and marketing channels. On the basis of these findings, it is suggested that the government and relevant departments should strengthen the construction of agricultural information platforms and websites. Authorities should also extend the training facilities of fruit farmers regarding the use of IIT, which could be useful to boost the capability of fruit farmers to develop markets and promote the value chain. Full article

(This article belongs to the Special Issue Digital Agriculture for Sustainable Food Systems: Implications for Land-Resource Use and Management)

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17 pages, 1917 KiB

Open AccessArticle

Factors on Spatial Heterogeneity of the Grain Production Capacity in the Major Grain Sales Area in Southeast China: Evidence from 530 Counties in Guangdong Province

by Wei Fang, Heliang Huang, Boxi Yang and Qiang Hu

Land 2021, 10(2), 206; https://doi.org/10.3390/land10020206 - 19 Feb 2021

Cited by 4 | Viewed by 2423

Abstract

Grain security is an essential issue for countries across the world. China has witnessed over the last decades not only a rapid growth in the volume of the grain production, but also a divergence in its geographical distributions. Existing studies on the influencing factors of grain production have overlooked thus spatial heterogeneity. This paper investigates the factors that cause the geographical heterogeneity in grain output levels in Guangdong province of China, in terms of land, labor and capital. To address the spatial attenuation effect of the influencing factors, we use the Geographically Weighted Regression (GWR) on samples of different spatial ranges, which include a total of 530 southern counties from 2015 to 2017. The results show that (a) the effect of land endowment on grain output vary across the east and the west, and between coastal and inland areas; (b) the effect of labor endowment on grain output are inconsistent in the sign and magnitude of the estimates across counties; (c) the effect of agricultural capital on grain production shows heterogeneity spatially (across the east and the west) and economically (across developed and less developed regions). We then analyze the potential mechanism behind this spatial heterogeneity, as well as its policy implications. Full article

(This article belongs to the Special Issue Digital Agriculture for Sustainable Food Systems: Implications for Land-Resource Use and Management)

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13 pages, 1478 KiB

Open AccessEditor’s ChoiceArticle

Exploring the Challenges Posed by Regulations for the Use of Drones in Agriculture in the African Context

by Matthew Ayamga, Bedir Tekinerdogan and Ayalew Kassahun

Land 2021, 10(2), 164; https://doi.org/10.3390/land10020164 - 06 Feb 2021

Cited by 28 | Viewed by 8967

Abstract

Global food demands have led to the rapid introduction of Information Communication Technology (ICT) innovations in the agriculture sector—one such innovation is drone technology. Drones are used in precision agriculture, including aerial observation, sensing, and the spraying of pesticides. Regulations on the use of drones are necessary because drones can violate privacy rules, data protection rights, and public peace. However, many African countries have either very restrictive regulations, or no proper regulation in place, making the process of acquiring a license for drone operation cumbersome. In this study, we present the results of a literature review that explores the current drone regulations in Sub-Saharan Africa and the results of a systematic literature review (SLR) and survey study whereby we have interviewed the relevant stakeholders, in order to understand the challenges posed by the regulations to the effective use of drones for agriculture. The results indicate that the regulations contain about 40 to 85 per cent of the provisions of the International Civil Aviation Organization (ICAO) manual on Remotely Piloted Aircraft Systems (RPASs). In addition, whilst the SLR focused on the technology, safety, ethics and regulatory hurdles towards drones, the interviewees focused on the need for skill and awareness among the responsible authorities to enforce regulations, and the need for sustainability and participatory process in defining regulations. Full article

(This article belongs to the Special Issue Digital Agriculture for Sustainable Food Systems: Implications for Land-Resource Use and Management)

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