Can We Use Machine Learning for Agricultural Land Suitability Assessment?

Möller, Anders; Mulder, Vera Leatitia; Heuvelink, G.B.M.; Jacobsen, Niels Mark; Greve, Mogens Humlekrog

doi:10.3390/agronomy11040703

Cited by 27 publications

(15 citation statements)

References 84 publications

(165 reference statements)

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“…The global spatial distribution of marginal land suitable for giant silvergrass in this study provides a macroscopic map from the perspective of environmental suitability. In practice, bioenergy production has complex interactions with other social and environmental systems [48]. Bioenergy development requires us to consider marginal land resources, the process of biomass energy production, ecological protection policy, energy conservation, and emission reduction benefits, and so on.…”

Section: Discussionmentioning

confidence: 99%

Using Machine Learning to Identify the Potential Marginal Land Suitable for Giant Silvergrass (Miscanthus × giganteus)

et al. 2022

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Developing biomass energy, seen as the most important renewable energy, is becoming a prospective solution in attempting to deal with the world’s sustainability-related challenges, such as climate change, energy crisis, and carbon emission reduction. As one of the most promising second-generation energy crops, giant silvergrass (Miscanthus × giganteus) is highly valued for its high potential for biomass production and low maintenance requirements. Mapping the potential global distribution of marginal land suitable for giant silvergrass is an essential prerequisite for the development of giant silvergrass-based biomass energy. In this study, a boosting regression tree was used to identify the marginal land resources for giant silvergrass cultivation using influencing factors, which include climate conditions, soil conditions, topography conditions, and land use. The results indicate that there are 3068.25 million hectares of land resources worldwide suitable for giant silvergrass cultivation, which are mainly located in Africa (902.05 million hectares), Asia (620.32 million hectares), South America (547.60 million hectares), and North America (529.26 million hectares). Among them, countries with the most land resources, Russia and Brazil, have the first- and second-highest amounts of suitable marginal land for giant silvergrass, with areas of 373.35 and 332.37 million hectares, respectively. Our results also rank the involved factors by their contribution. Climatic conditions have the greatest influence on the spatial distribution of giant silvergrass, with an average contribution of 74.38%, followed by land use, with a contribution of 17.38%. The contribution of the soil conditions is 7.26%. The results of this study provide instructive support for future biomass energy policy development.

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Section: Discussionmentioning

confidence: 99%

Using Machine Learning to Identify the Potential Marginal Land Suitable for Giant Silvergrass (Miscanthus × giganteus)

et al. 2022

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“…However, the expansion of agriculture over natural areas is not a sustainable solution, and the focus should therefore be to manage the land in a way that optimizes the usage and conservation of resources. Denmark has a long history of collecting information about land management and crop yields, and since 2011 the land use information has been available at the field level (94). The specific land use and management are typically the results of multiple factors, varying from environmental, economic, social and political factors.…”

Section: Agriculture and Forest Biomass Productionmentioning

confidence: 99%

Soil assessment in Denmark: Towards soil functional mapping and beyond

et al. 2023

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Soil provides essential ecosystem services sustaining and improving human life, but mapping soil functions is an ongoing challenge. Denmark has a long history of carrying out soil assessments − originally in order to determine tax revenues for the king, and, more recently, for aiding policymakers and farmers. This knowledge has supported the development of intensive agricultural systems while maintaining the provision of ecosystem services (e.g., clean water). Getting an overview of historical soil surveys and pedological mapping approaches can generate useful information for mapping soil, identifying gaps and proposing directions for future research. In this review, we explore the evolution of soil and environmental inventories, the historical development of soil mapping methods, and how these factors contributed to a better spatial understanding of soil functions. Specifically, we discuss soil functions related to water regulation (e.g., drainage, groundwater and water surface interactions, water table), water filtering (e.g., nitrogen leaching), carbon sequestration (e.g., peatlands), agricultural production (e.g., land suitability, wheat yields), and threats related to soil degradation (e.g., soil erosion). Denmark has benefitted from a government-coordinated approach, promoting detailed and systematic national soil surveys and environmental monitoring programmes. The large databases produced in the surveys formed the basis for mapping several soil properties and functions at increasingly high resolutions over the last many years based on developments in machine learning. In contrast to methodological advances in soil mapping and relevant contributions to pedometric research, we identified a lack of spatial information on soil biodiversity. Detailed spatial information about soil functions is essential to address global issues, such as climate change, food security and water security, and the experience of mapping soil functions in Denmark can be a source of inspiration to other parts of the world.

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“…Neural networks embed the process of imitating the operations of the human brain to perform tasks through a non-explicit programming structure, where sample data or training data are used to fetch insights from the available data resources. Machine learning is considered the subset of neural networks [50][51][52][53][54][55][56][57][58][59][60][61]. With the enormous amount of data, machine learning or artificial neural networks would help in identifying the pattern hidden inside the data.…”

Section: Artificial Neural Network and Machine Learning For Irrigationmentioning

confidence: 99%

“…Machine learning and artificial neural networks not only encourage researchers to make irrigation recommendations but also encourage the use of many other factors, such as crop suitability, yield prediction, plant disease classification, and profitable plantations [56,57]. Many of the previous research works have been enhanced with machine learning-based models for better classification and artificial intelligence-based predictions for more accuracy and efficiency [58,59].…”

Section: Artificial Neural Network and Machine Learning For Irrigationmentioning

confidence: 99%

Exploiting IoT and Its Enabled Technologies for Irrigation Needs in Agriculture

Veerachamy

Ramalakshmi

Kavin

et al. 2022

Water

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The increase in population growth and demand is rapidly depleting natural resources. Irrigation plays a vital role in the productivity and growth of agriculture, consuming no less than 75% of fresh water utilization globally. Irrigation, being the largest consumer of water across the globe, needs refinements in its process, and because it is implemented by individuals (farmers), the use of water for irrigation is not effective. To enhance irrigation management, farmers need to keep track of information such as soil type, climatic conditions, available water resources, soil pH, soil nutrients, and soil moisture to make decisions that resolve or prevent agricultural complexity. Irrigation, a data-driven technology, requires the integration of emerging technologies and modern methodologies to provide solutions to the complex problems faced by agriculture. The paper is an overview of IoT-enabled modern technologies through which irrigation management can be elevated. This paper presents the evolution of irrigation and IoT, factors to be considered for effective irrigation, the need for effective irrigation optimization, and how dynamic irrigation optimization would help reduce water use. The paper also discusses the different IoT architecture and deployment models, sensors, and controllers used in the agriculture field, available cloud platforms for IoT, prominent tools or software used for irrigation scheduling and water need prediction, and machine learning and neural network models for irrigation. Convergence of the tools, technologies and approaches helps in the development of better irrigation management applications. Access to real-time data, such as weather, plant and soil data, must be enhanced for the development of effective irrigation management applications.

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Can We Use Machine Learning for Agricultural Land Suitability Assessment?

Cited by 27 publications

References 84 publications

Using Machine Learning to Identify the Potential Marginal Land Suitable for Giant Silvergrass (Miscanthus × giganteus)

Using Machine Learning to Identify the Potential Marginal Land Suitable for Giant Silvergrass (Miscanthus × giganteus)

Soil assessment in Denmark: Towards soil functional mapping and beyond

Exploiting IoT and Its Enabled Technologies for Irrigation Needs in Agriculture

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