Soil suitability analysis and evaluation of pistachio orchard farming, using canonical multivariate analysis

Bodaghabadi, Mohsen Bagheri; Fasakhodi, Abbas Amini; Salehi, Mahdi; Hosseinifard, Seyed Javad; Heydari, Mojdeh

doi:10.1016/j.scienta.2018.10.069

Cited by 31 publications

(14 citation statements)

References 37 publications

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“…The first step in agricultural land use planning is land suitability assessment which is often conducted to determine which type of land use is suitable for a particular location [8]. Land suitability assessment is a method of land evaluation, which identifies the major limiting factors for planting a particular crop [9,10].…”

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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Section: Introductionmentioning

confidence: 99%

Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

et al. 2020

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“…PCA, another unsupervised pattern recognition method, was used to better understand the chemometric characteristics of different pericarps ( Figure 4 b). PCA reflects the importance of each variable in relation to the total variation on each axis; furthermore, it reveals the distribution states of different samples [ 29 , 30 ]. The sample distribution characteristics of prickly ash pericarps from 72 plantations are shown in Figure 4 a.…”

Section: Resultsmentioning

confidence: 99%

“…Long and Al have longer arrows compared to the other factors, indicating that they significantly influenced the alkylamide compositions of the prickly ash pericarps in the model. Moreover, the relationships between the samples are represented by the distances between two sites, and relationships between two variables are represented by the cosine values of two arrows [ 29 ]. The distributions of the sites were random and irregular.…”

Section: Resultsmentioning

confidence: 99%

Alkylamide Profiling of Pericarps Coupled with Chemometric Analysis to Distinguish Prickly Ash Pericarps

Tian

Wang

et al. 2021

Foods

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Because of extensive cultivation areas, various cultivars, nonstandard naming notations, and morphology similarity among relative cultivars, adulteration and associated business fraud may happen in the marketplaces of prickly ash pericarps due to higher financial gain and high-frequency trading. This study presents variations in the chemical components and contents of different prickly ash species from different plantations. Alkylamide profiling of pericarps derived from Zanthoxylum armatum, Z. bungeanum, and some relative Zanthoxylum species from 72 plantations across China were tested using ultra-performance liquid chromatography. Then, several chemometrics were applied to classify the prickly ash pericarps to reveal potential indicators that distinguish prickly ash pericarps and to identify the key factors that affect pericarp alkylamide profiling. The dominating alkylamides in the prickly ash pericarps were Z. piperitum (ZP)-amide C (0–20.64 mg/g) and ZP-amide D (0–30.43 mg/g). Alkylamide profiling of prickly ash pericarps varied significantly across species and geographical variations. ZP-amide D in prickly ash pericarps was identified as a potential indicator to distinguish prickly ash species. Longitude and aluminum content in soils were identified as key factors that affected alkylamide profiling of prickly ash pericarps. This study provides a useful tool to classify prickly ash species based on pericarp alkylamide profiling and to determine the key influence factors on pericarp alkylamide variations.

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“…The degree of influence of environmental factors is expressed by the arrow lengths in the figure: longer arrows represent greater influence on variations of the volatile oil profile in pericarps. Moreover, the relationships between two variables are represented by the cosine values of two arrows, and relationships between the samples are represented by the distances between two sites [27]. The distributions of the samples in each group were dispersive, indicating that selective environmental variables significantly influenced the volatile oil profile of the prickly ash pericarps in the model.…”

Section: The Influences Of Environmental Factors On Volatile Oil Profilementioning

confidence: 99%

Volatile Oil Profile of Prickly Ash (Zanthoxylum) Pericarps from Different Locations in China

Tian

Chen

et al. 2021

Foods

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Volatile oils of prickly ash (Zanthoxylum) pericarps have various potential biological functions with considerable relevance to food, pharmacological, and industrial applications. The volatile profile of oils extracted from prickly ash pericarps obtained from 72 plantations in China was determined by gas chromatography and mass spectrometry. Several chemometric analyses were used to better understand the volatile oil profile differences among different pericarps and to determine the key factors that affected geographical variations in the main volatile constituents of oils. A total of 47 constituents were detected with D-limonene, alfa-myrcene, and linalool as the most abundant. The volatile profile of pericarp oils was significantly affected by prickly ash species and some environmental factors, and the key factors that affected volatile profile variations for different prickly ash species were diverse. Chemometric analyses based on the volatile oil profile could properly distinguish Z. armatum pericarps from other pericarps. This study provides comprehensive information on the volatile oil profile of pericarps from different prickly ash species and different plantations, and it can be beneficial to a system for evaluating of pericarp quality. Moreover, this study speculates on the key environmental factors that cause volatile oil variations for each species, and can help to obtain better prickly ash pericarp volatile oils by improving the cultivated environments.

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Soil suitability analysis and evaluation of pistachio orchard farming, using canonical multivariate analysis

Cited by 31 publications

References 37 publications

Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

Alkylamide Profiling of Pericarps Coupled with Chemometric Analysis to Distinguish Prickly Ash Pericarps

Volatile Oil Profile of Prickly Ash (Zanthoxylum) Pericarps from Different Locations in China

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