Digital mapping of soil biological properties and wheat yield using remotely sensed, soil chemical data and machine learning approaches

Mahjenabadi, Vahid Alah Jahandideh; Mousavi, Seyed Roohollah; Rahmani, Asghar; Karami, Afsaneh; Rahmani, Hossein; Khavazi, K; Rezaei, Meisam

doi:10.1016/j.compag.2022.106978

Cited by 12 publications

(14 citation statements)

References 72 publications

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“… Jahandideh Mahjenabadi et al. (2022) conducted a comprehensive study in the Southwest of Iran, employing ML algorithms to predict the spatial distribution of soil biological properties and WY.…”

Section: Discussionmentioning

confidence: 99%

“…The Pasargad plain is a key agricultural zone in Iran that has been under continuous cultivation and exploitation for a long time. The dominant land use in the area is irrigated agriculture, with wheat being the main crop in the crop pattern schedule ( Jahandideh Mahjenabadi et al., 2022 ). So, according to this background, quantifying the production gaps of lands seems to be a very important plan for optimizing agricultural management and future land use planning.…”

Section: Methodsmentioning

confidence: 99%

“…Soil and environmental factors play a crucial role in determining WY ( Araus et al., 2003 ; Jahandideh Mahjenabadi et al., 2022 ). Factors such as soil nutrient content [exchangeable potassium, total nitrogen (TN), etc], pH levels, and organic matter composition affect soil fertility and nutrient availability, ultimately impacting the growth and health of wheat plants ( Norouzi et al., 2010 ; Zhang et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches

Mousavi,

Jahandideh Mahjenabadi,

Khoshru

et al. 2024

Front. Plant Sci.

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This study aimed to identify the most influential soil and environmental factors for predicting wheat yield (WY) in a part of irrigated croplands in southwest Iran, using the FAO-Agro-Climate method and machine learning algorithms (MLAs). A total of 60 soil samples and wheat grain (1 m × 1 m) in 1200 ha of Pasargad plain were collected and analyzed in the laboratory. Attainable WY was assessed using the FAO method for the area. Pearson correlation analysis was used to select the best set of soil properties for modeling. Topographic attributes and vegetation indices were used as proxies of landscape components and cover crop to map actual WY in the study area. Two well-known MLAs, random forest (RF) and artificial neural networks (ANNs), were utilized to prepare an actual continuous WY map. The k-fold method was used to determine the uncertainty of WY prediction and quantify the quality of prediction accuracy. Results showed that soil organic carbon (SOC) and total nitrogen (TN) had a positive and significant correlation with WY. The SOC, TN, normalized different vegetation index (NDVI), and channel network base level (CHN) were recognized as the most important predictors and justifying more than 50% of actual WY. The ANNs outperformed the RF algorithm with an R2 of 0.75, RMSE of 400 (kg ha−1), and RPD of 2.79, according to statistical indices. The uncertainty analysis showed that the maximum uncertainty of the prediction map [400 (kg ha−1)] was very low compared to the mean value [4937 (kg ha−1)] of WY map. Calculation yield gap using the FAO-agro-climatic model showed that the average yield gap of the region was about 50% of actual yield. The findings of this study demonstrated that integrating simulated attainable crop growth using crop model and a set of soil and environmental covariates with the ANNs algorithm can effectively predict WY gaps in large areas with acceptable and reasonable accuracy. The study emphasizes that the implementation of efficient management practices has the potential to enhance agricultural production in the study area and similar regions. These results represent a significant advancement of sustainable agriculture and provide valuable insights for ensuring global food security.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches

Mousavi,

Jahandideh Mahjenabadi,

Khoshru

et al. 2024

Front. Plant Sci.

Self Cite

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“…The data was interpolated once the number of sampling points was entered. The resultant map for each parameter was manually categorized using conventional ratings, with distinct colours assigned to each class [16].…”

Section: Generation Of Maps Using Geographic Information Systemmentioning

confidence: 99%

Assessment of Soil Quality and Nutrient index in Agroecological Unit 7 of Kerala and Mapping Using GIS

Gladis,

Ashok,

Joseph

et al. 2023

CJAST

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Aims: Soil quality assessment is valuable for agricultural production. In this study, 150 soil samples at 0-15 and 15-30 cm depths were collected from Kaipad land (Agroecological unit 7) of Kerala to assess the quality and nutrient index. Study Design: Survey, collection of sample and principal component analysis. Place and Duration of Study: Department of Soil Science and Agricultural Chemistry, College of Agriculture, Vellayani between 2020 and 2021. Methodology: Soil samples were collected from AEU 7 and analyzed for physical, chemical and biological indicators for evaluating soil quality. Principal component analysis (PCA) was carried out with 13 soil parameters which resulted in 6 principal components and a minimum data set (MDS) of nine parameters (pH, EC, organic carbon, available N, Mg, K, B, P and Zn) with eigen values greater than 1, which were used for computing soil quality index. Nutrient index was computed for organic carbon, available N, P and K. Thematic maps on SQI and NI were prepared using geographical information system (GIS). Results: The results revealed that the Kaipad soils (AEU 9) are sandy clay loam in texture, extreme to strong acid condition (pH 3.5- 5.0), high in organic carbon, low in available N and K, medium in available P, deficient in available Ca, Mg and B. Majority (88%) of the soils are rated medium in soil quality. Relative soil quality index ranged between 47.5 and 76.3 per cent in the Kaipad soils with a mean of 61.0 per cent. Conclusion: The soils of Kaipad (AEU 7) under different agricultural land uses experiences various constraints to crop production like extremely acidic pH, high electrical conductivity, . nutrient deficiencies of N, K, Ca, Mg, B and toxicities of Fe and Al. Examining the relationships of soil parameters by principal component analysis indicated that nine soil parameters such as pH, EC, organic carbon, available N, Mg, K, P, Zn and B have significantly contributed to soil quality index, which are the important key indicators of soil quality. The soil quality index was medium for Kaipad soil. Nutrient index was high for organic carbon, low for available N, medium for available P and low for available K.

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“…Similarly, Mod et al (2021) undertook spatial modelling of bacterial dynamics in the western Swiss Alps, considering factors such as elevation, slope, and aspect strata. Their studies establish that the environmental variables can serve as niche parameters for microbial biogeography studies (Lee et al, 2020;Lennon et al, 2012;Mahjenabadi et al, 2022). However, the distinct characteristics of microorganisms (e.g., small size, high diversity, sensitive interactions with the environment) had a substantial effect on the accuray of microbial spatial prediction.…”

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confidence: 99%

Drivers and human impacts on topsoil bacterial and fungal community biogeography across Australia

Xue,

Minasny,

Wadoux

et al. 2024

Global Change Biology

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Soil microbial diversity mediates a wide range of key processes and ecosystem services influencing planetary health. Our knowledge of microbial biogeography patterns, spatial drivers and human impacts at the continental scale remains limited. Here, we reveal the drivers of bacterial and fungal community distribution in Australian topsoils using 1384 soil samples from diverse bioregions. Our findings highlight that climate factors, particularly precipitation and temperature, along with soil properties, are the primary drivers of topsoil microbial biogeography. Using random forest machine‐learning models, we generated high‐resolution maps of soil bacteria and fungi across continental Australia. The maps revealed microbial hotspots, for example, the eastern coast, southeastern coast, and west coast were dominated by Proteobacteria and Acidobacteria. Fungal distribution is strongly influenced by precipitation, with Ascomycota dominating the central region. This study also demonstrated the impact of human modification on the underground microbial community at the continental scale, which significantly increased the relative abundance of Proteobacteria and Ascomycota, but decreased Chloroflexi and Basidiomycota. The variations in microbial phyla could be attributed to distinct responses to altered environmental factors after human modifications. This study provides insights into the biogeography of soil microbiota, valuable for regional soil biodiversity assessments and monitoring microbial responses to global changes.

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Digital mapping of soil biological properties and wheat yield using remotely sensed, soil chemical data and machine learning approaches

Cited by 12 publications

References 72 publications

Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches

Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches

Assessment of Soil Quality and Nutrient index in Agroecological Unit 7 of Kerala and Mapping Using GIS

Drivers and human impacts on topsoil bacterial and fungal community biogeography across Australia

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