Predicting soil temperatures under a ridge-furrow system in the U.S. Corn Belt

Gupta, Satish C.; Radke, J. K.; Swan, J. B.; Moncrief, J. F.

doi:10.1016/0167-1987(90)90056-j

Cited by 20 publications

(10 citation statements)

References 15 publications

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“…Radke (1982) also found that ridges dried faster in spring than furrows. Radke (1982) observed maximum soil temperature differences between ridges and furrows of 7 to 10 C at 5-cm depth in early May, and Gupta et al (1990) found differences of 3 to 5 C at 5-cm depth between ridge and furrow positions for maximum daily soil temperatures in late June in a ridge-furrow system, similar to the values noted here.…”

Section: Resultssupporting

confidence: 83%

“…Weed seed banks in ridge-tillage and conventional-till (moldboard plowing and associated tillage) systems may differ with respect to seed numbers and spatial distributions (Forcella andLindstrom 1988a, 1988b;Vanasse and Ler-oux 2000). In any tillage system, amount and type of residue (Buhler et al 1996;Mohler and Calloway 1992;Mohler 1993, 2000) and microtopography (Gupta et al 1990;Radke 1982) can alter microenvironmental conditions and influence weed establishment in different microtopographic positions. Ridge tillage creates a variable microtopography that may have different microenvironments.…”

Section: Introductionmentioning

confidence: 99%

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Microtopography, Microenvironments, and Weed Populations in Ridge-Tilled Soybean

Jurik¹

2006

Weed technol.

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The effects of microtopographic position on soil microenvironment and weed populations in ridge-tilled soybean were evaluated on three farms in Iowa in 1989 and 1990. In both years, over all weed species (primarily giant foxtail, green foxtail, yellow foxtail, redroot pigweed, and Pennsylvania smartweed), seedling emergence was highest in late May and early June, with few seedlings emerging after mid-June. Weed populations were highest in May and early June, after which rotary hoeing and cultivation reduced weed numbers in all plots. Microtopographic position (row, shoulder, and furrow) had a large effect on soil microenvironment and weed populations. Furrows were the wettest position through most of the growing season. Rows were the warmest position early in the season and the coolest position late in the season. Cumulative weed emergence early in the season was closely related to growing degree days, which accumulated faster in the row position than the furrow position. Following rotary hoeing and cultivation, the row position had significantly more total weeds than the shoulder and furrow positions on all farms in August of both years.

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Section: Resultssupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Microtopography, Microenvironments, and Weed Populations in Ridge-Tilled Soybean

Jurik¹

2006

Weed technol.

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“…Reestablishment of ridges in late June results in shallow mixing of surface soil, while maintaining cover. Surface residue cover in ridge tillage reduces soil erosion while the raised beds enhance soil warming in early spring (Gupta et al, 1990). One unknown consequence of ridge tillage is the effect of limited soil incorporation of amendments such as fertilizer and manure on nutrient losses in artificial drainage systems.…”

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confidence: 99%

Tillage and Nutrient Source Effects on Surface and Subsurface Water Quality at Corn Planting

et al. 2001

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This study quantified the effects of tillage (moldboard plowing [MP], ridge tillage [RT]) and nutrient source (manure and commercial fertilizer [urea and triple superphosphate]) on sediment, NH4+ -N, NO3- -N, total P, particulate P, and soluble P losses in surface runoff and subsurface tile drainage from a clay loam soil. Treatment effects were evaluated using simulated rainfall immediately after corn (Zea mays L.) planting, the most vulnerable period for soil erosion and water quality degradation. Sediment, total P, soluble P, and NH4+ -N losses mainly occurred in surface runoff. The NO3- -N losses primarily occurred in subsurface tile drainage. In combined (surface and subsurface) flow, the MP treatment resulted in nearly two times greater sediment loss than RT (P < 0.01). Ridge tillage with urea lost at least 11 times more NH4+ -N than any other treatment (P < 0.01). Ridge tillage with manure also had the most total and soluble P losses of all treatments (P < 0.01). If all water quality parameters were equally important, then moldboard plow with manure would result in least water quality degradation of the combined flow followed by moldboard plow with urea or ridge tillage with urea (equivalent losses) and ridge tillage with manure. Tillage systems that do not incorporate surface residue and amendments appear to be more vulnerable to soluble nutrient losses mainly in surface runoff but also in subsurface drainage (due to macropore flow). Tillage systems that thoroughly mix residue and amendments in surface soil appear to be more prone to sediment and sediment-associated nutrient (particulate P) losses via surface runoff.

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“…For this reason, many studies have focused on soil temperature modelling and forecasting. For instance, a model developed to forecast hourly soil temperature in the northern US Corn Belt, based on 1D and 2D heat flow equations, showed a 2-4 ∘ C difference from measured values (Gupta et al, 1990). Effects of vegetative cover on soil temperature were studied to enhance the accuracy of predicting thermal conditions in soil using a simultaneous heat and water model (Flerchinger and Pierson, 1991).…”

Section: Introductionmentioning

confidence: 99%

Forecasting soil temperature based on surface air temperature using a wavelet artificial neural network

et al. 2017

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Soil temperature is a very important variable in agricultural meteorology and strongly influences agricultural activities and planning (e.g. the date and depth of sowing crops, frost protection). There are many physically based studies in the literature which model soil temperature, but few are easily applicable for use in the field. Simple and precise short‐term forecasting of soil temperature with minimum data requirements is the main goal of this study. The soil temperature at 0300, 0900 and 1500 GMT was forecast based only on surface air temperatures using artificial neural network (ANN) and wavelet transform artificial neural network (WANN) models. The hourly data were collected from the Mashhad synoptic station in Khorasan Razavi province in Iran between 2010 and 2013. The results of this study showed that using a wavelet transform for preprocessing improved the accuracy of soil temperature forecasting. It was also found that changing the temporal increment in forecasting time did not have a noticeable effect on errors in the WANN models. WANN models can be used as accurate tools to forecast soil temperature 1–7 days ahead at depths of 5–30 cm.

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Predicting soil temperatures under a ridge-furrow system in the U.S. Corn Belt

Cited by 20 publications

References 15 publications

Microtopography, Microenvironments, and Weed Populations in Ridge-Tilled Soybean

Microtopography, Microenvironments, and Weed Populations in Ridge-Tilled Soybean

Tillage and Nutrient Source Effects on Surface and Subsurface Water Quality at Corn Planting

Forecasting soil temperature based on surface air temperature using a wavelet artificial neural network

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