Maize system impacts of cover crop management decisions: A simulation analysis of rye biomass response to planting populations in Iowa, U.S.A.

Marcillo, Guillermo; Carlson, Sarah; Filbert, Meghan; Kaspar, T. C.; Plastina, Alejandro; Miguez, Fernando E.

doi:10.1016/j.agsy.2019.102651

Cited by 22 publications

(23 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More details on this field scale model are available at http://www.apsim.info. The APSIM model has been successfully used and provided reasonable predictions of soil and crop aspects for Corn Belt conditions (Archontoulis et al., 2020; Marcillo et al., 2019).…”

Section: Methodsmentioning

confidence: 99%

“…info. The APSIM model has been successfully used and provided reasonable predictions of soil and crop aspects for Corn Belt conditions (Archontoulis et al, 2020;Marcillo et al, 2019). The model was initiated by setting up the operational management (irrigation, planting dates, crop harvest dates).…”

Section: Apsim Modeling Platformmentioning

confidence: 99%

“…Such models are capable of assessing crop impacts beyond the domain of a single field in a single year. The APSIM (Agricultural Production Systems sIMulator) platform is a biophysically‐based tool capable of simulating crop responses to altered management and climatic conditions (Holzworth et al., 2014; Keating et al., 2003) and related effects associated with cover crops grown in corn‐soybean crop rotations, as evidenced by recent studies (Basche et al., 2016; Dietzel, Liebman, Ewing, & Helmers, 2016; Li et al., 2008; Malone et al., 2017; Marcillo et al., 2019; Martinez‐Feria, Dietzel, Liebman, Helmers, & Archontoulis, 2016). However, to our knowledge, model‐based evaluations have not evaluated the potential of alterations to corn management for optimizing cover crop biomass, such as utilizing varied maturity season corn varieties or different corn planting dates.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simulating winter rye cover crop production under alternative management in a corn‐soybean rotation

et al. 2020

View full text Add to dashboard Cite

The Agricultural Production Systems sIMulator (APSIM) was used to evaluate two alternative approaches for extending the cover crop growing window into corn (Zea mays L.) and soybean (Glycine max L.) crop rotations in Nebraska, USA. We evaluated how: (i) shifting corn planting dates (mid-April to early-June) and (ii) altering comparative relative maturity (CRM) corn hybrids (80 to 115 days) influence cover crop biomass and corn yields over a 30-year period. The APSIM model was tested using experimental data and was then used to simulate a range of cover crop planting and termination scenarios. Our results showed no significant yield differences within the same corn relative maturity when planted on April 20 and May 13 but that yield declined when planted in June. During a six week fall cover crop planting window (September 15-October 31), every day before October 31 that the cover crop was planted resulted in additional 62 kg ha −1 of biomass. We also simulated a one month spring termination window (April 1-April 30) and, every day delay in cover crop termination resulted in per day additional 35 kg ha −1 of biomass. Cover crop biomass accrual was highly dependent on weather, where for identical fall planting dates, a warm wet season accrued approximately four times more biomass than a cool dry season. Although we found significant yield differences between early, medium and late season CRMs, earlier fall cover crop planting associated with either earlier spring corn planting or planting an early to medium season variety leads to tenfold greater cover biomass. Delayed corn planting by mid-May had no yield penalty relative to April planting, and could facilitate four-fold greater cover crop biomass (cover crop terminated April 30 instead of April 1). Our results demonstrate that earlier cover crop planting in fall or later cover crop termination in spring can result in significantly more biomass which can be balanced with yield goals.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Apsim Modeling Platformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simulating winter rye cover crop production under alternative management in a corn‐soybean rotation

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Other studies have used crop simulation models to simulate the crop–soil system and to predict cover crop biomass and N uptake at the field scale. In the upper Corn Belt region of the United States, for example, cereal rye cover crops have been simulated via mechanistic models for diverse goals such as (a) quantifying the relationship between cover crop biomass and water uptake of a corn–cereal rye rotation (Dietzel et al., 2016), (b) modeling soil water and N dynamics driven by soil changes affected by cover crop biomass (Martinez‐Feria, Dietzel, Liebman, Helmers, & Archontoulis, 2016), and (c) exploring how cover crop planting rates influence cereal rye biomass, soil N leaching, and crop productivity (Marcillo et al., 2019). Regardless of the level of detail embedded in a model, field performance must be adequately characterized to answer questions that help inform farm management decisions.…”

Section: Introductionmentioning

confidence: 99%

Using statistical learning algorithms to predict cover crop biomass and cover crop nitrogen content

et al. 2020

Self Cite

View full text Add to dashboard Cite

Cereal rye (Secale cereale sp.) is a cover crop species known to improve soil and water quality. Late-season biomass production is information growers need to maximize cover crop benefits and schedule field operations. Statistical learning, built upon statistical and computational algorithms that "learn" from data, may help to improve predictions of cover crop biomass as a function of initial soil inorganic nitrogen levels. Three models-Least Absolute Shrinkage and Selection Operator (LASSO), Ridge, and Random Forest (RF)-were trained and optimized on a 3-yr allometric and remote sensing dataset of cereal rye responses to N fertilization in the mid-Atlantic northern and southeastern United States. Shoot biomass (mean, 9,800 kg ha −1) was accurately predicted with a RF model (RMSE, 2,039 kg ha −1). Targeting shoot N content (mean, 107 kg ha −1), on the other hand, LASSO made accurate and more stable predictions (RMSE, 34 kg ha −1). Early-season information (cover crop C/N ratio, tiller counts, and ground-sensed normalized difference vegetation index) contributed to enhancing biomass and N content predictions. A final test on untrained data revealed that 92 and 73% of the predictions from either algorithm corresponded to ground-truthed biomass and shoot N content observed under different N regimes. Modern data-intensive approaches, such as statistical learning, show promise to characterize end-season performance of a cover crop and may contribute to better farm decision-making. 1 INTRODUCTION Building sustainable agricultural systems requires significant increases in agricultural production and the adoption

show abstract

“…For maize crop, the potential compensation among yield components might be expressed as a function of variations in plant population [1] and sowing dates [2], which will in turn condition prevailing regimes of meteorological variables throughout the whole crop growing season at a local scale [3]. Meteorological conditions in conjunction with management systems and plant populations lead to different productive potentials.…”

Section: Introductionmentioning

confidence: 99%

Maize Yield Components as Affected by Plant Population, Planting Date, Crop Growing Season and Soil Coverings in Brazil

Gc¹,

Lm²,

Pereira³

2020

Preprint

View full text Add to dashboard Cite

The potential yield of annual crops is affected by management practices and water and energy availabilities throughout the crop season. The current work aimed to assess the effects of plant population and soil covering on yield components of maize. Field experiments were carried out during 2014-15 and 2015-16 growing seasons at areas grown with oat straw, voluntary plants and bare soil, considering five different plant populations (40,000, 60,000, 80,000, 100,000 and 120,000 plants ha-1) and three sowing dates (15 Sep., 30 Oct., 15 Dec.) for the hybrid P30F53YH in Ponta Grossa, State of Parana, Brazil. Non-impacts of soil covering or plant population on plant height at the flowering phenological stage were observed. Significant effects of soil covering on crop physiological and yield components responses throughout the 2014-15 season were detected. Influence of plant populations on yield components was evidenced, suggesting that from 80,000 plants ha-1 the P30F53YH hybrid performs a compensatory effect among assessed yield components in such a way as to not compromise productivity insofar as plant population increases up to 120,000 plants ha-1. It was noticed a positive trend of yield components and crop final yield as a function of plant density increments.

show abstract

Maize system impacts of cover crop management decisions: A simulation analysis of rye biomass response to planting populations in Iowa, U.S.A.

Abstract: Maize system impacts of cover crop management decisions: A simulation analysis of rye biomass response to planting populations in Iowa, U.S.A.

Cited by 22 publications

References 36 publications

Simulating winter rye cover crop production under alternative management in a corn‐soybean rotation

Simulating winter rye cover crop production under alternative management in a corn‐soybean rotation

Using statistical learning algorithms to predict cover crop biomass and cover crop nitrogen content

Maize Yield Components as Affected by Plant Population, Planting Date, Crop Growing Season and Soil Coverings in Brazil

Contact Info

Product

Resources

About