Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling

Runoff is a very important phenomenon of hydrological cycle and it is relevant for the watershed management programme for conservation and development or natural resources and its management. However, In India the availability of accurate information on runoff is very scarce and needs to be calculated empirically for further developments. The Soil and Water Assessment Tool (SWAT) is a physical parameter model which has been developed to estimate values for the runoff, sediment and nutrient carry off from the agricultural watersheds under various distinct land management practices. For the present study, a small agricultural watershed has been selected for runoff assessment. Watershed is considered to be the ideal unit for management of the natural resources. Extraction of watershed parameters using Remote Sensing and Geographical Information System (GIS) and use of mathematical models is one of the current trends for hydrologic evaluation of these watersheds monitored. The Soil and Water Assessment Tool (SWAT) having an interface with Quantum GIS (QGIS) software (QSWAT version 1.3) was selected for the estimation of surface runoff from an area of Punpun basin near Patna an intermediate watershed of Punpun river, located in southern Bihar region. Maps of the region obtained from the Bihar Remote Sensing Application Centre (BIRSAC) were used for computation. The model was run and validated with the observed runoff and for the years 2005-2010. The performance of the model was evaluated using statistical and graphical methods to assess the capability of the model in simulating the surface runoff from the study area. According to the model estimates, the value for the surface runoff was maximum for the year 2007 as 710 mm and was minimum for the year 2005 with about 185 mm. As per the observed values of discharge from the Central Water Commission (CWC), the values for surface runoff for these years were different by about 10 to 11%.

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SWAT-predicted influence of different landscape and cropping system alterations on phosphorus mobility within the Pike River watershed of south-western Québec

Canadian Journal of Soil Science ◽

10.4141/s06-046 ◽

2007 ◽

Vol 87 (3) ◽

pp. 329-344 ◽

Cited By ~ 16

Author(s):

A. R. Michaud ◽

I. Beaudin ◽

J. Deslandes ◽

F. Bonn ◽

C. A. Madramootoo

Keyword(s):

Point Source ◽

Cover Crops ◽

Management Practices ◽

Assessment Tool ◽

Cropping System ◽

River Watershed ◽

Annual Crops ◽

Cropping Practices ◽

Water Assessment ◽

Soil And Water

An agreement between the governments of the province of Québec and the State of Vermont calls for a 41% decrease in phosphorus (P) loads reaching Missisquoi Bay, the northern portion of Lake Champlain. The agreement particularly targets the agricultural sector, since 80% of non-point source P inputs to the bay are associated with cultivated lands. In order to identify sustainable cropping practices likely to help meet the target P loads, the SWAT (soil and water assessment tool) model was employed to assess hydrological performance, erosion processes and P mobility on the bay’s principal Québec P contributing tributary, the 630 km2 Pike River watershed. Strong in-watershed spatial clustering of vulnerability to non-point source exports highlights the need for targeted implementation of sustainable agricultural practices and soil conservation works to derive the reduction in P loads. Planting cover crops over the 10% most vulnerable lands would result in roughly a 21% d rop in overall P exports at the watershed outlet, whereas the same 10% randomly distributed over the watershed would only contribute to a 6% drop in P exports. The study of different field-scale management scenarios indicated that achieving the targeted 41% reduction in P exports would require the widespread (half the land devoted to annual crops) implementation of sustainable cropping practices, and the conversion of a specific 10% of the territory to either cover crops or permanent prairie land. Meeting the P target-loads would require additional investments in the protection of floodplains and riparian strips, the targeted construction of runoff-control structures, and the rapid soil incorporation of manures on lands dedicated to annual crops. Key words: Soil and water assessment tool, modelling, sediment, phosphorus, cropping system, scenario, best agricultural management practices

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Detecting Winter Cover Crops and Crop Residues in the Midwest US Using Machine Learning Classification of Thermal and Optical Imagery

Remote Sensing ◽

10.3390/rs13101998 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1998

Author(s):

Mallory Liebl Barnes ◽

Landon Yoder ◽

Mahsa Khodaee

Keyword(s):

Remote Sensing ◽

Cover Crops ◽

Management Practices ◽

Crop Residues ◽

Regional Scale ◽

Climate Mitigation ◽

Near Infrared Reflectance ◽

Machine Learning Classification ◽

Winter Cover ◽

Winter Cover Crops

Cover crops are an increasingly popular practice to improve agroecosystem resilience to climate change, pests, and other stressors. Despite their importance for climate mitigation and soil health, there remains an urgent need for methods that link winter cover crops with regional-scale climate mitigation and adaptation potential. Remote sensing is ideally suited to provide these linkages, yet, cover cropping has not been analyzed extensively in remote sensing research. Methods used for remote sensing of crops from satellites traditionally leverage the difference between visible and near-infrared reflectance to isolate the signal of photosynthetically active vegetation. However, using traditional greenness indices like the Normalized Difference Vegetation Index (NDVI) for remotely sensing winter vegetation, such as winter cover crops, is challenging because vegetation reflectance signals are often confounded with reflectance of bare soil and crop residues. Here, we present new and established methods of detecting winter cover crops using remote sensing observations. We find that remote sensing methods that incorporate thermal data in addition to traditional reflectance metrics are best able to distinguish between winter farm management practices. We conclude by addressing the potential of existing and upcoming hyperspectral and thermal missions to further assess agroecosystem function in the context of global change.

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