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- Delavar, Mohammad Amir, et al.
(författare)
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Soil salinity mapping by remote sensing south of Urmia Lake, Iran
- 2020
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Ingår i: Geoderma Regional. - : Elsevier. - 2352-0094. ; 22
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Tidskriftsartikel (refereegranskat)abstract
- Urmia Lake is a shallow terminal Lake located in northwest Iran and it is one of the largest permanent Lakes in the Middle East. In this study, the changes in soil salinity at Urmia Lake were investigated using satellite images and the oldest salinity map of the area over a period of 45 years from 1973 to 2018. The distribution of salinity in 2018 was estimated using the supervised classification by the nonlinear hybrid model of artificial multi-layered neural network-genetic algorithm model (ANN-GA) while the salinity map for the years of 1985, 1995, 2005 and 2015 was estimated by the unsupervised method. Further, the salinity data of surface soil in the region for the year 1973 was also digitized and utilized. For this purpose, 291 surface samples (258 samples for modeling and 33 samples for the re-evaluation of the model) of the studied region were collected and analyzed in 2018. The input neurons were selected by analyzing the satellite imagery bands, salinity indices, salinity ratio index and normalized difference vegetation index. The correlation coefficient and root-mean-square error of the training network model were equal to 0.94 and 0.04, respectively. The salinity map of the studied region was estimated using this model and classified into six classes (S0 to S5). The produced map of 2018 was used to re-evaluate the results. It showed that lower estimation accuracy was in classes S1 and S2. The obtained results in this study indicated that roughness, moisture, the density of halophyte plants and sodium slickspot were some of the sources for estimation of errors in lower salinity classes. The time-series changes in the salinity class of estimated maps showed that S3, S4 and S5 classes have expanded between 1973 and 2018. These are in agreement with the field observation and with the other scientific reports about the studied area.
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| 2. |
- Muoni, Tarirai, et al.
(författare)
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Critical slope length for soil loss mitigation in maize-bean cropping systems in SW Kenya
- 2020
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Ingår i: Geoderma. - : Elsevier BV. - 0016-7061 .- 1872-6259. ; 22
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Tidskriftsartikel (refereegranskat)abstract
- Soil erosion and land fragmentation threaten agricultural production of sub-Saharan African highlands. At our study site in Western Kenya, farm size is mostly < 2 ha, laid out in narrow strips in slope direction and ploughed downhill. Soil conservation measures like hedgerows and green manures can reduce effective slope length for erosion, but compete with crops for space and labour. Knowledge of critical slope length can minimise interventions and trade-offs. Hence, a maize-bean intercrop (MzBn) slope length trial on 20, 60 and 84 m long plots, replicated twice on three farms was carried out in Rongo, Migori County, during one rainy season. Soil loss from 84 m slope length (SL) plots was 250 % higher than from 60 m and 710% higher than from 20 m plots, while soil loss from 20 and 60 m plots did not differ (p < 0.05). Conversely, runoff was lower on the 84 m than on the 60 m (p < 0.05) or the 20 m SL (p < 0.05). Across all three farms slope gradient and length had highest explanatory power to predict soil loss. At individual farm level, under similar slope and soil texture, slope length and profile curvature were most influential. Regarding results of the slope length experiments, food crop plot lengths < 50 m appear essential considering soil loss, sediment load, and soil loss to yield ratio under the given rainfall, soil and slope (10-14%) conditions. Our results call for designing integrating slope length options and cropping systems for effective soil conservation. We recommend planting Mucuna and Calliandra-hedgerows as buffer strips below the critical slope length, and legume cash crops and maize uphill. Such approaches are critical against the backdrop of land fragmentation and labour limitation to sustainably maximise food production from the available land area in the region. (c) 2020 Elsevier B.V. All rights reserved.
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