Assessing Spatial Variability of Soil Properties in Mediterranean Smallholder Farming Systems

Kosma, Chariklia; Triantafyllidis, Vassilios; Zotos, Anastasios; Pittaras, Antonios; Kouneli, Varvara; Karydogianni, Stella; Mavroeidis, Antonios; Kakabouki, Ioanna; Beslemes, Dimitrios; Tigka, Evangelia; Roussis, Ioannis; Bilalis, Dimitrios

doi:10.3390/land11040557

Cited by 8 publications

(8 citation statements)

References 52 publications

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“…This and other CMs are simplified descriptions of natural systems, being limited by the lack of information regarding some variables (e.g., production, field, soil, culture, biotic and abiotic factors, cultural itinerary) [23]. In Mediterranean agricultural areas, there is great spatial variability in the physical and chemical properties of the soil, even in the smallest fields [3]. This increased uncertainty in the simulations can reduce the model's overall accuracy.…”

Section: Modeling Plant Nutrition and Requirementsmentioning

confidence: 99%

“…Input management should also be improved by applying precise doses of nitrogen (N) to each specific point of the field [2]. It is necessary to avoid the uncontrolled and injudicious use of fertilizers without a strategy or method, as the use of nutrient site-specific management is essential to improving soil fertility and crop productivity [3]. A good management strategy can include the use of mineral fertilizers combined with organic manures to increase the total nitrogen stock in the soil, guaranteeing increases in production over time and, consequently, higher net revenues.…”

Section: Introductionmentioning

confidence: 99%

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Remote Monitoring of Crop Nitrogen Nutrition to Adjust Crop Models: A Review

et al. 2023

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Nitrogen use efficiency (NUE) is a central issue to address regarding the nitrogen (N) uptake by crops, and can be improved by applying the correct dose of fertilizers at specific points in the fields according to the plants status. The N nutrition index (NNI) was developed to diagnose plant N status. However, its determination requires destructive, time-consuming measurements of plant N content (PNC) and plant dry matter (PDM). To overcome logistical and economic problems, it is necessary to assesses crop NNI rapidly and non-destructively. According to the literature which we reviewed, it, as well as PNC and PDM, can be estimated using vegetation indices obtained from remote sensing. While sensory techniques are useful for measuring PNC, crop growth models estimate crop N requirements. Research has indicated that the accuracy of the estimate is increased through the integration of remote sensing data to periodically update the model, considering the spatial variability in the plot. However, this combination of data presents some difficulties. On one hand, at the level of remote sensing is the identification of the most appropriate sensor for each situation, and on the other hand, at the level of crop growth models is the estimation of the needs of crops in the interest stages of growth. The methods used to couple remote sensing data with the needs of crops estimated by crop growth models must be very well calibrated, especially for the crop parameters and for the environment around this crop. Therefore, this paper reviews currently available information from Google Scholar and ScienceDirect to identify studies relevant to crops N nutrition status, to assess crop NNI through non-destructive methods, and to integrate the remote sensing data on crop models from which the cited articles were selected. Finally, we discuss further research on PNC determination via remote sensing and algorithms to help farmers with field application. Although some knowledge about this determination is still necessary, we can define three guidelines to aid in choosing a correct platform.

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Section: Modeling Plant Nutrition and Requirementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote Monitoring of Crop Nitrogen Nutrition to Adjust Crop Models: A Review

et al. 2023

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“…As the global food demand and the world population rapidly increase, cultivated soil conditions are gradually deteriorating, leading to a decline in its potential [1][2][3]. Soil salinization is a major factor affecting global crop yield reduction; more than 8 × 10 8 hm 2 of land worldwide is affected by salinization, accounting for more than 6% of the total land area [4,5].…”

Section: Introductionmentioning

confidence: 99%

Spatial Variations and Distribution Patterns of Soil Salinity at the Canal Scale in the Hetao Irrigation District

Hu,

Miao,

Shi

et al. 2023

Water

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Soil salinization is a major factor impacting global crop yields. To explore the spatial distribution and influencing factors of soil water and salt in typical canals of the Hetao irrigation district, regional soil information was monitored at fixed locations. In this study, classical statistics, geostatistics, and spatial autocorrelation methods were used to conduct quantitative analyses of soil salt content, water content, soil particle size distribution, and groundwater depth. The variation coefficient of the soil salt content in the 20–40 and 40–60 cm soil layers was between 10% and 100%, which corresponds to a medium degree of variation; the other soil layers had strong degrees of variation. The soil moisture content in each layer varied moderately. The gold coefficients of soil salt content and water content were less than 0.25, and the Z value was greater than 0, showing a strong spatial correlation and certain spatial agglomeration characteristics, which were mainly affected by structural factors in the study area. The distribution patterns of soil water and salt were affected by soil particle size. Sand content decreased with increasing depth, soil salt was negatively correlated with sand content, and soil water was positively correlated with sand content. Soil salinity was significantly affected by groundwater depth and increased with decreasing groundwater depth, following an exponential relationship. When the groundwater depth exceeded 1.7 m, the soil salt content exhibited small changes with groundwater depth. The results of this study could play a guiding role in terms of understanding the degree of soil salinization surrounding canals in the Hetao irrigation area and adjusting land management strategies over time.

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“…The spatial variability of agricultural lands is closely related to the values of soil quality indices, and expresses the degree of heterogeneity of soil and agricultural lands (Kosma et al, 2022). For the evaluation of the variability of the natural or anthropic terrestrial areas (agricultural lands, periurban or urban areas) are used both the classical methods, based on the analysis of soil or plant samples, as well as methods based on remote sensing and imaging analysis (Datcu et al, 2017;Herbei and Sala, 2020;Popescu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Characterization of Some Agricultural Lands Based on Agrochemical Indices. Case Study in the Area of Cenei Locality

AGAPIE¹,

HORABLAGA²,

Bostan³

et al. 2022

LSSD

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The study evaluated the fertility and spatial variability of some agricultural lands, in the area of Cenei locality, Timis county, Romania. The soil samples, representative for the studied agricultural lands, were taken from nine different locations. The soil reaction (pH, H2O), total nitrogen content (Nt), mobile potassium content (K, ppm) were considered and studied, and the degree of saturation in basic cations (V,%) was calculated. The soil reaction showed values between pH = 6.29 - 7.97 ±0.20, the degree of saturation in basic cations (V,%) had values between V = 74.80 - 100.00 ±3.13%, total nitrogen showed values between Nt = 0.11 - 0.35 ±0.03%, and the potassium content showed values between K = 275 - 390 ±15.49 ppm. High variability was recorded in the Nt index values (CVNt = 38.3598) and low variability in the soil reaction (CVpH = 8.3248). For the other soil indices studied, intermediate values were recorded, respectively CVV = 9.9760 for the degree of saturation in basic cations (V%), and CVK = 13.7177 in the case of mobile potassium content (Kmobil). The variation of the mobile K content in relation to the soil reaction (pH) was described by a polynomial equation of degree 2, equation (1), in statistical safety conditions, at the level of R2 = 0.779, p = 0.0121. Under PCA, PC1 explained 69.593% of variance, and PC2 explained 25.919% of variance. Cluster analysis facilitated the grouping of soil samples into two separate clusters, with three sub-clusters, under Coph. coeff. = 0.859.

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Assessing Spatial Variability of Soil Properties in Mediterranean Smallholder Farming Systems

Cited by 8 publications

References 52 publications

Remote Monitoring of Crop Nitrogen Nutrition to Adjust Crop Models: A Review

Remote Monitoring of Crop Nitrogen Nutrition to Adjust Crop Models: A Review

Spatial Variations and Distribution Patterns of Soil Salinity at the Canal Scale in the Hetao Irrigation District

Characterization of Some Agricultural Lands Based on Agrochemical Indices. Case Study in the Area of Cenei Locality

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