Geostatistical and fuzzy clustering approach for delineation of site-specific management zones and yield-limiting factors in irrigated hot arid environment of India

Moharana, Pravash Chandra; Jena, Roomesh Kumar; Pradhan, Upendra Kumar; Nogiya, Mahaveer; Tailor, B. L.; Singh, R. S.; Singh, Surendra

doi:10.1007/s11119-019-09671-9

Cited by 54 publications

(19 citation statements)

References 35 publications

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“…In the context of oil plantation management, the stratification of soil diversity was necessary to facilitate the best practice maintenance since it correlated to efficient investment for fertilization cost (Rhebergen et al 2020). The identical cluster indicated similar soil properties in the site (Moharana et al 2020). Thus, it could be managed using equal treatment.…”

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confidence: 75%

Soil diversity influences on oil palm productivity in ultramafic ecosystems, Southeast Sulawesi, Indonesia

et al. 2020

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Abstract. Alam S, Purwanto BH, Hanudin E, Putra ETS. 2020. Soil diversity influences oil palm productivity in ultramafic ecosystems, Southeast Sulawesi, Indonesia. Biodiversitas 21: 5521-5530. Ultramafic is a unique ecosystem with an environmental gradient due to its high soil diversity. Understanding soil diversity in ultramafic ecosystems is fundamentally required to support sustainable agriculture management, particularly in oil palm plantation. This study investigated the relationship between soil diversity and productivity of oil palm in ultramafic ecosystems, Southeast Sulawesi. The soil survey has taken in Oheo, Langgikima, and Routa districts. Soil sample was collected from various toposequence, including upper, middle, and lower slope to describe soil properties, including soil texture, pH, CEC, SOC, TN, Av-P, Exc-K, Exc-Ca, and Exc-Mg. Whilst observation of oil palm productivity was done by recording the fresh-weight of fruit yield in hectare unit during a month. The results showed that the soil characteristics from the three sites were significantly different in soil texture, pH, CEC, Av-P, Exc-Ca, and Exc-Mg. A similar trend was also recorded in oil palm productivity wherein the highest oil palm productivity was discovered in Oheo (556.41+96.26 kg ha-1), followed by Langgikima (501.11+54.84 kg ha-1) and Routa (163.19+17.55 kg ha-1). Our study founded: pH, CEC, Av-P, Exc-K, Exc-Ca, and Exc-Mg significantly affected the productivity of oil palm. Based on these results, the soil diversity highly affected oil palm productivity in ultramafic ecosystems.

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confidence: 75%

Soil diversity influences on oil palm productivity in ultramafic ecosystems, Southeast Sulawesi, Indonesia

et al. 2020

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“…CaO amounts necessary to increase the pH value from a suboptimal range (lime supply statuses A and B) to the optimum pH range (lime supply status C), as recommended by the VDLUFA approach (CaOVDLUFA, see Figure 3), in comparison to the CaO amounts predicted by the stepless approach (CaOModel). Regarding the lime supply status C, the task was to relate the lime recommendation linearly to the pH, decreasing from the lowest CaO amount recommended in lime supply status B to zero as recommended if the pH achieves lime supply status D. Therefore, the upper pH limits of the lime supply statuses B (pHmax_B) and C (pHmax_C) were applied according to Equations ( 4) and (5). The minimum CaO demand (CaOmin_B) of lime supply level B, which refers to its maximum pH (pHmax_B), can be calculated from Equations (2), ( 7) and (8).…”

Section: Figurementioning

confidence: 99%

“…Therefore, it can be economically and environmentally inefficient [1]. Sitespecific management, as an intrinsic part of precision agriculture, contributes to increasing yield, reducing inputs, and minimizing environmental impact [2][3][4][5][6][7]. However, the benefits of site-specific management strategies are strongly dependent on the quality of the variability prediction.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Lime Demand of Arable Soils from pH Value, Soil Texture and Soil Organic Matter Content

2021

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For more than 40 years, farmers in Germany have used the fertilization recommendation schema provided by the Association of German Agricultural Investigation and Research Institutions (VDLUFA) to quantify the required lime (CaO) demand of arable mineral soils. To be applicable as guidelines in practice, the results of 30 years of fertilization experiments that studied the correlation between crop yields and the actual soil pH, the soil texture, and the soil organic matter (SOM) content were finally condensed into a look-up table system. However, because the original experimental data are no longer accessible, the purpose of this study is to reconstruct the interaction between the three soil parameters and their appropriate lime demands. Therefore, the class-based, stepwise approach of the look-up table system is transferred into a continuous, stepless approach using mathematical models. Under the precondition to preserve the pH-, texture- and SOM-dependent CaO amounts recommended in the look-up system (n = 317) to the greatest extent possible, the algorithm was successful; more than 99% of their variability could be explained by the models. This adaptation helps to meet the accuracy of present-day requirements of precision farming.

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“…Generally, MZ delineation approaches can be categorized based on the provided data and information from different sources [2]. These methods are generally based on farmers' knowledge [20], soil physical and chemical attributes [21][22][23][24], geomorphology [25], yield [26][27][28][29][30][31], electrical conductivity (EC) [32,33] and RS [17,34,35] data, and also hybrid models that combine information from different data sources [36][37][38][39][40][41][42][43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat

Rokhafrouz

Latifi

Abkar³

et al. 2021

Agriculture

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Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample T-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times.

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Geostatistical and fuzzy clustering approach for delineation of site-specific management zones and yield-limiting factors in irrigated hot arid environment of India

Cited by 54 publications

References 35 publications

Soil diversity influences on oil palm productivity in ultramafic ecosystems, Southeast Sulawesi, Indonesia

Soil diversity influences on oil palm productivity in ultramafic ecosystems, Southeast Sulawesi, Indonesia

Predicting the Lime Demand of Arable Soils from pH Value, Soil Texture and Soil Organic Matter Content

Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat

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