Land Suitability Analysis for Potato Crop in the Jucusbamba and Tincas Microwatersheds (Amazonas, NW Peru): AHP and RS–GIS Approach

Trigoso, Daniel Iliquín; López, Rolando Salas; Briceño, Niltón B. Rojas; López, Jhonsy O. Silva; Fernández, Darwin Gómez; Oliva, Manuel; Huatangari, Lenin Quiñones; Murga, Renzo E. Terrones; Castillo, Elgar Barboza; Gurbillón, Miguel Ángel Barrena

doi:10.3390/agronomy10121898

Cited by 27 publications

(21 citation statements)

References 46 publications

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“…In practice, land evaluation has made use of widely different methods. Studies may focus on the climate [11,12] or the soil [13,14], and they may include socioeconomic variables [15,16].…”

Section: Introductionmentioning

confidence: 99%

Can We Use Machine Learning for Agricultural Land Suitability Assessment?

et al. 2021

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It is vital for farmers to know if their land is suitable for the crops that they plan to grow. An increasing number of studies have used machine learning models based on land use data as an efficient means for mapping land suitability. This approach relies on the assumption that farmers grow their crops in the best-suited areas, but no studies have systematically tested this assumption. We aimed to test the assumption for specialty crops in Denmark. First, we mapped suitability for 41 specialty crops using machine learning. Then, we compared the predicted land suitabilities with the mechanistic model ECOCROP (Ecological Crop Requirements). The results showed that there was little agreement between the suitabilities based on machine learning and ECOCROP. Therefore, we argue that the methods represent different phenomena, which we label as socioeconomic suitability and ecological suitability, respectively. In most cases, machine learning predicts socioeconomic suitability, but the ambiguity of the term land suitability can lead to misinterpretation. Therefore, we highlight the need for increasing awareness of this distinction as a way forward for agricultural land suitability assessment.

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“…In practice, land evaluation has made use of widely different methods. Studies may focus on the climate [11,12] or the soil [13,14], and they may include socioeconomic variables [15,16].…”

Section: Introductionmentioning

confidence: 99%

Can We Use Machine Learning for Agricultural Land Suitability Assessment?

et al. 2021

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“…In land suitability studies, methods such as multi-criteria decision-making methods, analytical hierarchy process, crop simulation models and machine learning related methods are the most popular and dynamic [42]. Remote sensing, geostatistics and geographic information systems were also successfully employed for determining land suitability of important crops in other countries [43]. Land suitability mapping using geochemical and spatial analysis methods was also used in cases of soils contaminated with toxic elements [44].…”

Section: Discussionmentioning

confidence: 99%

Optimized Land Use through Integrated Land Suitability and GIS Approach in West El-Minia Governorate, Upper Egypt

et al. 2021

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Land evaluation is imperative for its efficient use in agriculture. Therefore, this study aimed at assessing the suitability of a region in West El-Minia for cultivating some of the major crops using the geographical information system (GIS). The results focus on allocating space for cultivating sugar beet and utilizing the free period of sugar beet in other crops. This exploitation helps to maintain the quality of the land and increase its fertility by using crop rotation with integrated agricultural management. A machine learning technique was implemented using the random forest algorithm (RF) to predict soil suitability classes for sugar beet using geomorphology, terrain attribute and remote sensing data. Fifteen major crops were evaluated using a suitability multicriteria approach in GIS environment for crop rotation decisions. Soil parameters were determined (soil depth, pH, texture, CaCO3, drainage, ECe, and slope) to characterize the land units for soil suitability. Soils of the area were found to be Entisols; Typic Torrifluvents, Typic Torripsamments and Typic Torriorthents and Aridsols; Typic Haplocacids, Calcic Haplosalids and Sodic Haplocalcids. Overall, the studied area was classified into four suitability classes: high “S1”, moderate “S2”, marginal “S3”, and not suitable “N”. The area of each suitability class changed depending on the crop tested. The highest two crops that occupied S1 class were barley with 471.5 ha (representing 6.8% of the total study area) and alfalfa with 157.4 ha (2.3%). In addition, barley, sugar beet, and sorghum occupied the highest areas in S2 class with 6415.3 ha (92.5%), 6111.3 ha (88.11%) and 6111.3 ha (88.1%), respectively. Regarding the S3 class, three different crops (sesame, green pepper, and maize) were the most highly represented by 6151.8 ha (88.7%), 6126.3 ha (88.3%), and 6116.7 ha (88.2%), respectively. In the end, potato and beans occupied the highest areas in N class with 6916.9 ha (99.7%) and 6853.5 ha (98.8%), respectively. The results revealed that the integration of GIS and soil suitability system consists of an appropriate approach for the evaluation of suitable crop rotations for optimized land use planning and to prevent soil degradation. The study recommends using crop rotation, as it contributes to soil sustainability and the control of plant pests and diseases, where the succession of agricultural crops on a scientific basis aims at maintaining the balance of nutrients and fertilizers in the soil.

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“…This framework does not consider the different impact of ecological criteria on the land suitability assessment process, whereas each criterion has its contribution. For calculating the weight of criteria, the analytical hierarchy process (AHP) developed by Saaty (1980) was broadly used (Iliquín Trigoso et al, 2020 andSalas López et al, 2020). The importance of each criterion was estimated using an aggregation of experts' opinions.…”

Section: Gis-based Multiple Criteria Analysis For Land Suitability Assessmentmentioning

confidence: 99%

“…Many ranking methods were used for this purpose, such as Analytical Hierarchy Process (Fekadu and Negese, 2020), Analytical Network Process (Seyedmohammadi et al, 2019), Weighted Linear Combination (Yin et al, 2020), and Ordered Weighted Average (Zabihi et al, 2019). However, the AHP method has been the most widely applied agricultural land suitability analysis (Iliquín Trigoso et al, 2020). To calculate the weights of criteria, using Pairwise Comparison Matrix (Table 1), where the relative importance of criteria is evaluated from 1 to 9 (Saaty, 1980).…”

Section: Gis-based Multiple Criteria Analysis For Land Suitability Assessmentmentioning

confidence: 99%

“…It enables extensive information about land performances to land users, land-use planners, and agricultural support services, making a critical decision for specific purposes to increase productivity (AbdelRahman et al, 2016). Numerous crop management programs carried out land suitability assessment (LSA) to locate potential areas for the cultivation of various crops, such as rice (Özkan et al, 2019), maize (Habibie et al, 2021), potato (Iliquín Trigoso et al, 2020), rubber (Ali et al, 2018), and coffee (Salas López et al, 2020). These studies conclude that LSA considered the importance of land suitability assessment a pivotal step in selecting potential cultivated plants and areas.…”

Section: Introductionmentioning

confidence: 99%

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A GIS-Based Multicriteria Analysis of Land Suitability for Groundnut Crop in Nghe An Province, Vietnam

2021

IJG

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This study focuses on identifying the potential lands for growing groundnut in Dien Chau district of Nghe An province (Vietnam), where groundnut is one of the major crops and brings high income for farmers. Based on the ecological requirements of groundnut, six criteria, including Soil Type, Soil Texture, Soil Depth, Slope, Average Temperature, and Average Total Rainfall in the planting season, were used. The Analytic Hierarchy Process method, commonly used in agricultural land use planning, was utilized to determine each criterion's weights via experts’ opinions. A pairwise comparison matrix was established to support this assessment process. The results revealed that Soil Texture showed the highest weight (0.31727) for groundnut farming, which was followed by Average Temperature (0.21131), Soil Type (0.17426), and Soil Depth (0.13982). Slope and Average Total Rainfall were the lowest weight factors, with 0.08122 and 0.07612, respectively. The weighted sum overlay analysis was implemented by ArcGIS software to generate the spatial distribution of land suitability of groundnut. The land suitability map indicated that 6830.07 ha (22.26%) of the studied area was highly suitable (S1), 10413.85 ha (33.95%) was moderately suitable (S2), 4336.76 ha (14.14%) was marginally suitable (S3), and 424.99 ha (1.39%) was not suitable (N). The total area of constrained area, including Waterbody and Built-up Land, was 8671.39 ha, accounting for 28.27% of the total area. Finally, the proposed land for groundnut cultivation was 12928.69 ha. The outcomes of this study may be regarded as a good reference for local government in agricultural land use planning.

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Land Suitability Analysis for Potato Crop in the Jucusbamba and Tincas Microwatersheds (Amazonas, NW Peru): AHP and RS–GIS Approach

Cited by 27 publications

References 46 publications

Can We Use Machine Learning for Agricultural Land Suitability Assessment?

Can We Use Machine Learning for Agricultural Land Suitability Assessment?

Optimized Land Use through Integrated Land Suitability and GIS Approach in West El-Minia Governorate, Upper Egypt

A GIS-Based Multicriteria Analysis of Land Suitability for Groundnut Crop in Nghe An Province, Vietnam

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