Net Primary Production of U.S. Midwest Croplands from Agricultural Harvest Yield Data

Prince, Stephen D.; Haskett, Jonathan; Steininger, Marc K.; Strand, Holly; Wright, R.

doi:10.2307/3061021

Cited by 68 publications

(112 citation statements)

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“…The model was calibrated with observed monthly streamflow and nitrate flux, and annual crop yields from 1994 to 2006. Model configuration and calibration followed the procedures described by Hu et al (2007), except the farming schedule given by Tonitto et al (2007a) described above was used, and the harvest index for soybean was adjusted from the default value of 0.31-0.42, the average value suggested by Prince et al (2001). The latter change reduced the simulated soybean N fixation to 99 kg N ha -1 year -1 , which is slightly less than the range reported in the literature for the region.…”

Section: Swatmentioning

confidence: 99%

Modeling denitrification in a tile-drained, corn and soybean agroecosystem of Illinois, USA

et al. 2008

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Denitrification is known as an important pathway for nitrate loss in agroecosystems. It is important to estimate denitrification fluxes to close field and watershed N mass balances, determine greenhouse gas emissions (N 2 O), and help constrain estimates of other major N fluxes (e.g., nitrate leaching, mineralization, nitrification). We compared predicted denitrification estimates for a typical corn and soybean agroecosystem on a tile drained Mollisol from five models (DAYCENT, SWAT, EPIC, DRAINMOD-N II and two versions of DNDC, 82a and 82h), after first calibrating each model to crop yields, water flux, and nitrate leaching. Known annual crop yields and daily flux values (water, nitrate-N) for 1993-2006 were provided, along with daily environmental variables (air temperature, precipitation) and soil characteristics. Measured denitrification fluxes were not available. Model output for 1997-2006 was then compared for a range of annual, monthly and daily fluxes. Each model was able to estimate corn and soybean yields accurately, and most did well in estimating riverine water and nitrate-N fluxes (1997-2006 mean measured nitrate-N loss 28 kg N ha -1 year -1 , model range 21-28 kg N ha -1 year -1 ). Monthly patterns in observed riverine nitrate-N flux were generally reflected in model output (r 2 values ranged from 0.51 to 0.76). Nitrogen fluxes that did not have corresponding measurements were quite variable across the models, including 10-year average denitrification estimates, ranging from 3.8 to 21 kg N ha -1 year -1 and substantial variability in simulated soybean N 2 fixation, N harvest, and the change in soil organic N pools. DNDC82a and DAYCENT gave comparatively low estimates of total denitrification flux (3.8 and 5.6 kg N ha -1 year -1 , respectively) with similar patterns controlled primarily by moisture. DNDC82h predicted similar fluxes until 2003, when estimates were abruptly much greater. SWAT and DRAINMOD predicted larger denitrification fluxes (about 17-18 kg N ha -1 year -1 ) with monthly values that were similar. EPIC denitrification was intermediate between all models (11 kg N ha -1 year -1 ). Predicted daily fluxes during a high precipitation year (2002) varied considerably among models regardless of whether the models had comparable annual fluxes for the years. Some models predicted large denitrification fluxes for a few days, whereas others predicted large fluxes persisting for several weeks to months. Modeled denitrification fluxes were controlled mainly by soil moisture status and nitrate available to be denitrified, and the way denitrification in each model responded to moisture status greatly determined the flux. Because denitrification is dependent on the amount of nitrate available at any given time, modeled differences in other components of the N cycle (e.g., N 2 fixation, N harvest, change in soil N storage) no doubt led to differences in predicted denitrification. Model comparisons suggest our ability to accurately predict denitrification fluxes (without known values) from the dominant...

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Section: Swatmentioning

confidence: 99%

Modeling denitrification in a tile-drained, corn and soybean agroecosystem of Illinois, USA

et al. 2008

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“…Estimates of residue C inputs were made for the nine most dominant crops in the US, that together make up over 90% of the total US harvested cropland area: alfalfa (Medicago sativa L.) hay, barley (Hordeum vulgaris L.), corn (Zea mays L.) for grain, corn for silage and green chop, oats (Avena sativa L.), other hay (hay other than alfalfa; i.e., tame hay, small grain hay, wild hay), sorghum (Sorghum bicolor), soybean (Glycine max L.), and wheat (Triticum aestivum L.). Using the approach presented in earlier studies, (Bolinder et al 1999;Clapp et al 2000;Prince et al 2001;Allmaras et al 2004), we estimated the above-and belowground crop biomass and residue C inputs using algorithms (Table 1) based on cropspecific harvest indices and root:shoot ratios from published biomass partitioning studies (Lawes 1977;Wych and Stuthman 1983;Buyanovsky and Wagner 1986;Russell 1991;Bolinder et al 1997Bolinder et al , 1999Juma et al 1997;Peters et al 1997;Pierce and Fortin 1997;Vanotti et al 1997;Campbell and de Jong 2001;IPCC 2006). Yield data were first corrected for water content and total (aboveground and belowground) residue dry matter (kg ha -1 ) was estimated from yields (reported as tons acre -1 for hay and silage crops and bushels acre -1 for grain crops) using the algorithms given in Table 1.…”

Section: Estimation Of Crop Npp and Residue C Inputsmentioning

confidence: 99%

“…US crop yield data, collected annually by the National Agricultural Statistics Service (NASS) have also been used in broad-scale crop NPP estimates (Prince et al 2001;Lobell et al 2002;Johnson et al 2006). One drawback of the standard NASS data is missing data and gaps in coverage.…”

Section: Introductionmentioning

confidence: 99%

Carbon balances in US croplands during the last two decades of the twentieth century

et al. 2010

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Carbon (C) added to soil as organic matter in crop residues and carbon emitted to the atmosphere as CO 2 in soil respiration are key determinants of the C balance in cropland ecosystems. We used complete and comprehensive county-level yields and area data to estimate and analyze the spatial and temporal variability of regional and national scale residue C inputs, net primary productivity (NPP), and C stocks in US croplands from 1982 to 1997. Annual residue C inputs were highest in the North Central and Central and Northern Plains regions that comprise *70% of US cropland. Average residue C inputs ranged from 1.8 (Delta States) to 3.0 (North Central region) Mg C ha -1 year -1 , and average NPP ranged from 3.1 (Delta States) to 5.4 (Far West region) Mg C ha -1 year -1 . Residue C inputs tended to be inversely proportional to the mean growing season temperature. A quadratic relationship incorporating the growing season mean temperature and total precipitation closely predicted the variation in residue C inputs in the North Central region and Central and Northern Plains. We analyzed the soil C balance using the crop residue database and the Introductory Carbon Balance regional Model (ICBMr). Soil C stocks (0-20 cm) on permanent cropland ranged between 3.07 and 3.1 Pg during the study period, with an average increase of *4 Tg C year -1 , during the 1990s. Interannual variability in soil C stocks ranged from 0 to 20 Tg C (across a mean C stock of 3.08 ± 0.01 Pg) during the study period; interannual variability in residue C inputs varied between 1 and 43 Tg C (across a mean input of 220 ± 19 Tg). Such interannual variation has implications for national estimates of CO 2 emissions from cropland soils needed for implementation of greenhouse gas (GHG) mitigation strategies involving agriculture.

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“…1) (Lal et al 1997), thus the net primary production (NPP) would be 7 Mg C ha -1 year -1 . This is consistent with NPP values for corn grown in North America, mostly in the U.S. Midwest, which were calculated to be 7.6 Mg C ha -1 year -1 using data from Prince et al (2001). In Fig.…”

Section: Introductionmentioning

confidence: 68%

“…In temperate regions, corn plants can achieve a height of more than 2.25 m and an aboveground biomass of 15.9-24.0 Mg ha -1 (silage) in a growing season (Jones 2003). An additional 3-5 Mg ha -1 of biomass is accumulated in the root system (Prince et al 2001), which is especially interesting in the context of soil C storage because corn roots are not removed at harvest (Amos and Walters 2006). An example based on a corn agroecosystem in southern Ontario, Canada shows that the crop fixes 10 Mg C ha -1 year -1 through photosynthesis and loses 3 Mg C ha -1 year -1 through respiration ( Fig.…”

Section: Introductionmentioning

confidence: 99%

Crop residue chemistry, decomposition rates, and CO2 evolution in Bt and non-Bt corn agroecosystems in North America: a review

Yanni

Whalen

2009

Nutr Cycl Agroecosyst

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Net Primary Production of U.S. Midwest Croplands from Agricultural Harvest Yield Data

Cited by 68 publications

References 0 publications

Modeling denitrification in a tile-drained, corn and soybean agroecosystem of Illinois, USA

Modeling denitrification in a tile-drained, corn and soybean agroecosystem of Illinois, USA

Carbon balances in US croplands during the last two decades of the twentieth century

Crop residue chemistry, decomposition rates, and CO2 evolution in Bt and non-Bt corn agroecosystems in North America: a review

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