Soil and water losses along the cultivation cycle of onion in Irati, Brazil

Antoneli, Valdemir; Paula, Enaiale Caroline de; Bednarz, João Anésio; Rodrigo‐Comino, Jesús; Cerdà, Artemi; Pulido, Manuel

doi:10.1016/j.catena.2021.105439

Cited by 2 publications

(2 citation statements)

References 46 publications

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“…With the rapid development of economy, environmental problems such as air pollution [1][2][3], energy shortages [4] and water and soil loss [5][6] begin to appear. These problems are particularly serious in the mining area [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Identification and Monitoring of Surface Elements in Open-Pit Coal Mine Area Based on Multi-Source Remote Sensing Images

Hai¹,

Xia²,

Song³

et al. 2022

Pol. J. Environ. Stud.

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Coal mining has brought a series of environmental problems. Local government departments have issued relevant governance policies, but the premise of scientific prevention and control is to correctly grasp the actual distribution of various ground objects in the mining area. Using classification methods to extract ground object information based on remote sensing images can effectively realize mining area monitoring and provide reference for land and space planning and environmental protection in the mining area. Therefore, it is very important to select the appropriate scale and method to identify the ground object information of remote sensing image. in this paper, Landsat 8 images of the Wucaiwan mining area and GF-2 images of the Tebian coal mine were taken as the research objects, and unsupervised classification, supervised classification and object-oriented classification were used to identify and monitor the mining area's surface. The results showed that: (1) the classification effect of the Mahalanobis distance method was the best in terms of comprehensive operation process and classification accuracy. This method had high classification accuracy for GF-2 and Landsat 8 images. When classifying GF-2 images, the kappa coefficient reached 0.90, and the overall classification accuracy was 94.27%. When classifying Landsat 8 images, the kappa coefficient reached 0.85, and the overall classification accuracy was 90.02%. (2) The factors causing the classification error were 'homospectral foreign bodies' and 'mixed pixels'. (3) When combined with the actual needs and image characteristics, the extensive use of medium and high-resolution remote sensing images to identify and monitor the surface elements of mining areas can greatly improve the work efficiency and minimize the image costs. (4) The construction layout of tailings pond in the Tebian coal mine was conducive

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Section: Introductionmentioning

confidence: 99%

Identification and Monitoring of Surface Elements in Open-Pit Coal Mine Area Based on Multi-Source Remote Sensing Images

Hai¹,

Xia²,

Song³

et al. 2022

Pol. J. Environ. Stud.

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“…Shallot plants in the horticultural plant require fertilizers and pesticides to grow and develop [1], [2]. Pesticide is the main choice for controlling pests, diseases, and weeds because of its capability to kill the corpses directly [3], [4].…”

Section: Introductionmentioning

confidence: 99%

The Behavior of Shallot Farmer Using Pesticides in Lembah Gumanti District, Solok Regency, Indonesia

Elfianto¹,

Syahni²,

Asmawi³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Shallot plants in the horticultural plant require fertilizers and pesticides to grow and develop. This research focuses on the factors that influence the behavior of shallot farmers in the use of pesticides using the approach of predisposition, enabling, and reinforcing factors. This research was conducted using a quantitative descriptive approach in Lembah Gumanti, Alahan Panjang Subdistrict, Solok Regency, West Sumatra. The selection of Solok Regency was carried out purposively because Solok is the largest shallot production area in West Sumatra. Data were analyzed using Partial Least Square structural equation modeling. This research showed that (1) dispositioning factor do not have a significant effect on the behavior of shallot farmers in using pesticides; (2) enabling factor has a significant effect on the behavior of shallot farmers in using pesticides; (3) reinforcing factors have a significant effect on the behavior of shallot farmers in using pesticides; (4) Disposing of factors has a significant effect on the intention of shallot farmers in using pesticides; (5) intention has a significant effect on the behavior of shallot farmers in using pesticides. Thus, our study discovered that dispositioning traits have no obvious effect on the pesticide use behavior of shallot producers. However, enabling, reinforcing, and disposal variables and intention all have a significant effect on shallot producers' pesticide use. Further research is recommended using the interview to obtain comprehensive results regarding the dispositioning factors that do not significantly affect the behavior of shallot farmers in using pesticides.

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Soil and water losses along the cultivation cycle of onion in Irati, Brazil

Cited by 2 publications

References 46 publications

Identification and Monitoring of Surface Elements in Open-Pit Coal Mine Area Based on Multi-Source Remote Sensing Images

Identification and Monitoring of Surface Elements in Open-Pit Coal Mine Area Based on Multi-Source Remote Sensing Images

The Behavior of Shallot Farmer Using Pesticides in Lembah Gumanti District, Solok Regency, Indonesia

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