Digital soil assessment for regional agricultural land evaluation

Harms, Ben; Brough, Dan; Philip, Seonaid; Bartley, Rebecca; Clifford, David; Thomas, Mark; Willis, R.; Gregory, Linda

doi:10.1016/j.gfs.2015.04.001

Cited by 24 publications

(17 citation statements)

References 16 publications

(22 reference statements)

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“…At the same time, government agencies are requested to provide land resource information for land use intensification, and irrigation development (Kidd et al, 2012;Harms et al, 2015). Digital soil mapping techniques combined with a land suitability framework facilitated the rapid evaluation of regional-scale agricultural potential in remote area (Harms et al, 2015).…”

Section: Capacity Building and Training: An Example From Experiences mentioning

confidence: 99%

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Digital soil mapping: A brief history and some lessons

2016

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Section: Capacity Building and Training: An Example From Experiences mentioning

confidence: 99%

“…Digital soil mapping techniques combined with a land suitability framework facilitated the rapid evaluation of regional-scale agricultural potential in remote area (Harms et al, 2015).…”

Section: Capacity Building and Training: An Example From Experiences mentioning

confidence: 99%

Digital soil mapping: A brief history and some lessons

2016

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“…Auxiliary variables included eight environmental variables (including elevation, slope, soil erosion, sediment retention, length of flow, ratio of evapotranspiration to precipitation, water yield, and wetness index), three socioeconomic variables, and land cover. Harms et al [39] assessed land suitability for irrigated crops for 155,000 km 2 of northern Australia using digital mapping approaches and machine learning models. They concluded that the coupling of digitally derived soil and land attributes with a conventional land suitability framework facilitates the rapid evaluation of regional-scale agricultural potential in a remote area.…”

Section: Introductionmentioning

confidence: 99%

Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

et al. 2020

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Land suitability assessment is essential for increasing production and planning a sustainable agricultural system, but such information is commonly scarce in the semi-arid regions of Iran. Therefore, our aim is to assess land suitability for two main crops (i.e., rain-fed wheat and barley) based on the Food and Agriculture Organization (FAO) “land suitability assessment framework” for 65 km2 of agricultural land in Kurdistan province, Iran. Soil samples were collected from genetic layers of 100 soil profiles and the physical-chemical properties of the soil samples were analyzed. Topography and climate data were also recorded. After calculating the land suitability classes for the two crops, they were mapped using machine learning (ML) and traditional approaches. The maps predicted by the two approaches revealed notable differences. For example, in the case of rain-fed wheat, results showed the higher accuracy of ML-based land suitability maps compared to the maps obtained by traditional approach. Furthermore, the findings indicated that the areas with classes of N2 (≈18%↑) and S3 (≈28%↑) were higher and area with the class N1 (≈24%↓) was less predicted in the traditional approach compared to the ML-based approach. The major limitations of the study area were rainfall at the flowering stage, severe slopes, shallow soil depth, high pH, and large gravel content. Therefore, to increase production and create a sustainable agricultural system, land improvement operations are suggested.

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“…However, soil is complex and can vary quite erratically in the context of space and time (Webster 2000), and subsequent model-based predictions of soil phenomena are anything but 'error free' (Brown and Heuvelink 2005). In noting this, Harms et al (2015) provided additional mapping of a confidence measure (based on Mahalanobis distance calculations) of the suitability classifications. They were able to state explicitly where suitability classifications were likely to be good and where they were likely to be uncertain.…”

Section: Brief Review Of Land Suitability Evaluationmentioning

confidence: 99%

“…It has been observed in recent times, an increasing activity in land resource assessments that incorporate some sort of digital soil (and sometimes climate) mapping. Recent examples include Kidd et al (2012) in Tasmania, Australia; Harms et al (2015) in Queensland, Australia;and van Zijl et al (2014) in Mozambique. One reason perhaps for this activity is that one can derive with digital soil and climate modeling, very attribute specific mapping.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Taking account of uncertainties in digital land suitability assessment (v0.1)"

2015

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Simulations are used to generate plausible realisations of soil and climatic variables for input into an enterprise land suitability assessment (LSA). Subsequently we present a case study demonstrating a LSA (for hazelnuts) which takes into account the quantified uncertainties of the biophysical model input variables. This study is carried out in the Meander Valley Irrigation District, Tasmania, Australia. It is found that when comparing to a LSA that assumes inputs to be error free, there is a significant difference in the assessment of suitability. Using an approach that assumes inputs to be error free, 56% of the study area was predicted to be suitable for hazelnuts. Using the simulation approach it is revealed that there is considerable uncertainty about the 'error free' assessment, where a prediction of 'unsuitable' was made 66% of the time (on average) at each grid cell of the study area. The cause of this difference is that digital soil mapping of both soil pH and conductivity have a high quantified uncertainty in this study area. Despite differences between the comparative methods, taking account of the prediction uncertainties provide a realistic appraisal of enterprise suitability. It is advantageous also because suitability assessments are provided as continuous variables as opposed to discrete classifications. We would recommend for other studies that consider similar FAO (Food and Agriculture Organisation of the United Nations) land evaluation framework type suitability assessments, that parameter membership functions (as opposed to discrete threshold cutoffs) together with the simulation approach are used in concert.

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Digital soil assessment for regional agricultural land evaluation

Cited by 24 publications

References 16 publications

Digital soil mapping: A brief history and some lessons

Digital soil mapping: A brief history and some lessons

Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

Peer Review #1 of "Taking account of uncertainties in digital land suitability assessment (v0.1)"

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