Use of site specific farming systems and computer simulation models for agricultural productivity and environmental quality

Bakhsh, Allah

doi:10.31274/rtd-180813-13909

Cited by 2 publications

(4 citation statements)

References 32 publications

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“…No N-treatment was applied during the soybean phase of production. More detail about experimental layout and yield data can be found in Bakhsh (1999), Jaynes et al (1999b), and Colvin (1990). Primary tillage after harvest consisted of moldboard plow in 1996 and 1997 and chisel plow in 1998.…”

Section: Site Description and Experimental Designmentioning

confidence: 99%

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Simulating Effects of Variable Nitrogen Application Rates on Corn Yields and No3 n Losses in Subsurface Drain Water

Bakhsh¹,

Kanwar²,

Jaynes³

et al. 2001

Transactions of the ASAE

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Using a model as a management tool requires testing of the model against field-measured data prior to its application for solving natural resource problems. This study was conducted to test the Root Zone Water Quality Model (RZWQM98) using four years (1996 to 1999) of field-measured data to simulate the effects of different N-application rates on corn yields and nitrate-nitrogen (NO3-N) losses via subsurface drain water. Three N-application rates (low, medium, and high), each replicated three times, were applied to corn in 1996 and 1998 under a randomized complete block design at a tile-drained corn-soybean rotation field near Story City, Iowa. No N-fertilizer was applied to soybean in 1997 and 1999. Model calibration and evaluation were based on field measurements of tile flows, NO3-N losses in tile water, and corn-soybean yields. On average, the model simulated tile flow, NO3-N losses in tile water, and yields by showing a percent difference of-8%, 15%, and-4%, respectively, between measured and simulated values. The simulated yield response function showed that corn grain yields reached a plateau level when the N-application rate exceeded 200 kg-N/ha in 1996 and 170 kg-N/ha in 1998. These results suggest that RZWQM has the potential to simulate the effects of N-application rates on corn yields and NO3-N losses with tile water. However, the model overestimated NO3-N losses in subsurface drainage water during the soybean growth period, which may require further refinements in the N-cycling algorithm in relation to N2-fixation and N-uptake processes.

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Section: Site Description and Experimental Designmentioning

confidence: 99%

“…input to the model (table 2). The detail of these measurements can be found in Bakhsh (1999). The management data for tillage operations, planting and harvesting dates, and fertilizer applications (table 1) were also used as model inputs.…”

Section: Input Data For Rzwqmmentioning

confidence: 99%

Simulating Effects of Variable Nitrogen Application Rates on Corn Yields and No3 n Losses in Subsurface Drain Water

Bakhsh¹,

Kanwar²,

Jaynes³

et al. 2001

Transactions of the ASAE

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“…Therefore, cropping systems need to be developed which can improve agricultural efficiency while meeting environmental goals. One such system has been recognized as precision farming (Bakhsh, 1999).Several studies have shown the promising potential of precision farming for economical and environmental benefits (Power et al, 1998;Blackmore, 1994;Brown and Steckler, 1995). The motive behind this technology is making use of the spatial variability of fields.…”

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confidence: 99%

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confidence: 99%

Using Soil Attributes and Gis for Interpretation of Spatial Variability in Yield

Bakhsh¹,

Colvin²,

Jaynes³

et al. 2000

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Precision farming application requires better understanding of variability in yield patterns in order to determine the cause-effect relationships. This field study was conducted to investigate the relationship between soil attributes and corn (Zea mays L.)-soybean (Glycine max L.) yield variability using four years yield data from a 22-ha field located in central Iowa. Corn was grown in this field during 1995, 1996, and 1998, and soybean was grown in 1997. Yield data were collected on nine east-west transects, consisting of 25-yield blocks per transect. To compare yield variability among crops and years, yield data were normalized based on N-fertilizer treatments. The soil attributes of bulk density, cone index, organic matter, aggregate uniformity coefficient, and plasticity index were determined from data collected at 42 soil sampling sites in the field. Correlation and stepwise regression analyses over all soil types in the field revealed that Tilth Index, based upon soil attributes, did not show a significant relationship with the yield data for any year and may need modifications. The regression analysis showed a significant relationship of soil attributes to yield data for areas of the field with Harps and Ottosen soils. From a geographic information system (GIS) analysis performed with ARC/INFO, it was concluded that yield may be influenced partly by management practices and partly by topography for Okoboji and Ottosen soils. Map overlay analysis showed that areas of lower yield for corn, at higher elevation, in the vicinity of Ottosen and Okoboji soils were consistent from year to year; whereas, areas of higher yield were variable. From GIS and statistical analyses, it was concluded that interaction of soil type and topography influenced yield variability of this field. These results suggest that map overlay analysis of yield data and soil attributes over longer duration can be a useful approach to delineate subareas within a field for site specific agricultural inputs by defining the appropriate yield classes. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.This article is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/abe_eng_pubs/517 T oday's scientists are facing many challenges in developing strategies for sustainable crop production systems. The focus of earlier efforts in the 1960s increased crop yield by twofold or more by applying high-yield agricultural inputs (Bottrell and Weil, 1995). These inputs were comprised of biological inputs (crop varieties), mechanical inputs (farm mechanization), water inputs (irrigation systems), and chemical inputs. Use of chemical inputs such as herbicides, insecticides, fungicides, and fertilizers have become an integral part of the high-yield package despite some of their negative effects on the environment. This high-input strategy has been successful in narrowing the gap between food and fib...

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Use of site specific farming systems and computer simulation models for agricultural productivity and environmental quality

Cited by 2 publications

References 32 publications

Simulating Effects of Variable Nitrogen Application Rates on Corn Yields and No3 n Losses in Subsurface Drain Water

Simulating Effects of Variable Nitrogen Application Rates on Corn Yields and No3 n Losses in Subsurface Drain Water

Using Soil Attributes and Gis for Interpretation of Spatial Variability in Yield

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Use of site specific farming systems and computer simulation models for agricultural productivity and environmental quality

Cited by 2 publications

References 32 publications

Simulating Effects of Variable Nitrogen Application Rates on Corn Yields and No3 n Losses in Subsurface Drain Water

Simulating Effects of Variable Nitrogen Application Rates on Corn Yields and No3 n Losses in Subsurface Drain Water

Using Soil Attributes and Gis for Interpretation of Spatial Variability in Yield

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Simulating Effects of Variable Nitrogen Application Rates on Corn Yields and No3 n Losses in Subsurface Drain Water

Simulating Effects of Variable Nitrogen Application Rates on Corn Yields and No3 n Losses in Subsurface Drain Water