Simulating the effects of spatially variable irrigation on corn yields, costs, and revenue in Iowa

DeJonge, Kendall C.; Kaleita, Amy L.; Thorp, Kelly R.

doi:10.1016/j.agwat.2007.05.008

Cited by 40 publications

(22 citation statements)

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“…Although VRI offers significant potential, a comprehensive method to predict the magnitude of benefits has not been developed. Previous research quantified some benefits from a small number of intensely studied fields using simulation (Nijbroek et al, 2003;DeJonge et al, 2007;Hedley and Yule, 2009) or experimentation (King et al, 2006;Khalilian et al, 2008;Hillyer and Higgins, 2014). It is unclear how field-specific research from the small number of fields studied can be extrapolated to assist VRI investment decisions for the spatial variability found in the larger set of farmer fields.…”

Section: Or If Undepleted Water Is Mined Early In the Season And The mentioning

confidence: 99%

Pumpage Reduction by Using Variable-Rate Irrigation to Mine Undepleted Soil Water

Heeren²,

Martin

et al. 2016

Trans. ASABE

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Section: Or If Undepleted Water Is Mined Early In the Season And The mentioning

confidence: 99%

Pumpage Reduction by Using Variable-Rate Irrigation to Mine Undepleted Soil Water

Heeren²,

Martin

et al. 2016

Trans. ASABE

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“…DeJonge et al [14] investigated the effect of variable rate irrigation management on corn production in Iowa using the CERES-maize model. Corn yield was compared for a period of 28 years under simulated scenarios of no irrigation, scheduled uniform irrigation and precision irrigation.…”

Section: Simulationsmentioning

confidence: 99%

“…Research has been mainly focused on the sensing and control aspects of precision irrigation with much advancements in the last decade [12]. Research is limited, however, in the development of appropriate irrigation scheduling tools for the precision irrigation process [14]. Irrigation scheduling is the process by which a producer determines when to apply irrigation and the amount of irrigation water to apply [15].…”

Section: Introductionmentioning

confidence: 99%

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

et al. 2017

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Globally, the irrigation of crops is the largest consumptive user of fresh water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water but do not compromise crop quality and yield. Precision irrigation already achieves this goal, in part. The goal of precision irrigation is to accurately supply the crop water need in a timely manner and as spatially uniformly as possible. However, to maximize the benefits of precision irrigation, additional technologies need to be enabled and incorporated into agriculture. This paper discusses how incorporating adaptive decision support systems into precision irrigation management will enable significant advances in increasing the efficiency of current irrigation approaches. From the literature review, it is found that precision irrigation can be applied in achieving the environmental goals related to sustainability. The demonstrated economic benefits of precision irrigation in field-scale crop production is however minimal. It is argued that a proper combination of soil, plant and weather sensors providing real-time data to an adaptive decision support system provides an innovative platform for improving sustainability in irrigated agriculture. The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil-plant-atmosphere system while considering operational limitations and agronomic objectives in arriving at optimal irrigation decisions. It is concluded that significant improvements in crop yield and water savings can be achieved by incorporating model predictive control into precision irrigation decision support tools. Further improvements in water savings can also be realized by including deficit irrigation as part of the overall irrigation management strategy. Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks that can be applied to evaluate adaptive decision support strategies related to irrigation.

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“…More routinely, crop growth models are used for post-hoc analysis of data from past field experiments, which culminate as an effort in model evaluation (Farahani et al, 2009;Guerra et al, 2004;Modala et al, 2015;Thorp et al, 2014b). Once evaluated, models are often applied as simulation tools to address a variety of research questions related to irrigation management (Baumhardt et al, 2009;Cammarano et al, 2012;DeJonge et al, 2007;Kisekka et al, 2016;McCarthey et al, 2014;Nair et al, 2013), crop water use (Droogers, 2000), or regional water management issues (Guerra et al, 2007;Yang et al, 2010). Use of models in this way can extend knowledge beyond the findings of field research, but the efforts certainly embody an exercise in extrapolation, with associated limiting assumptions.…”

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confidence: 99%

Cotton Irrigation Scheduling Using a Crop Growth Model and FAO-56 Methods: Field and Simulation Studies

Thorp¹,

Hunsaker²,

Bronson³

et al. 2017

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Simulating the effects of spatially variable irrigation on corn yields, costs, and revenue in Iowa

Cited by 40 publications

References 25 publications

Pumpage Reduction by Using Variable-Rate Irrigation to Mine Undepleted Soil Water

Pumpage Reduction by Using Variable-Rate Irrigation to Mine Undepleted Soil Water

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Cotton Irrigation Scheduling Using a Crop Growth Model and FAO-56 Methods: Field and Simulation Studies

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