Integrating growth stage deficit irrigation into a process based crop model

López, José R.; Winter, Jonathan M.; Elliott, Joshua; Ruane, Alex C.; Porter, Cheryl; Hoogenboom, Gerrit

doi:10.1016/j.agrformet.2017.05.001

Cited by 46 publications

(24 citation statements)

References 45 publications

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“…Due to drought associated with climate change, agricultural water resources will be reduced, it is necessary to achieve maximum production per unit of applied irrigation water. As one of the important ways to enhance water-use efficiency, regulated deficit irrigation may solve this dilemma [1][2][3][4]. However, to achieve a delicate balance between yield and irrigation water, effective monitoring methods for crop water stress are necessary [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing

et al. 2019

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Mapping maize water stress status and monitoring its spatial variability at a farm scale are a prerequisite for precision irrigation. High-resolution multispectral images acquired from an unmanned aerial vehicle (UAV) were used to evaluate the applicability of the data in mapping water stress status of maize under different levels of deficit irrigation at the late vegetative, reproductive and maturation growth stages. Canopy temperature, field air temperature and relative humidity obtained by a handheld infrared thermometer and a portable air temperature/relative humidity meter were used to establish a crop water stress index (CWSI) empirical model under the weather conditions in Ordos, Inner Mongolia, China. Nine vegetation indices (VIs) related to crop water stress were derived from the UAV multispectral imagery and used to establish CWSI inversion models. The results showed that non-water-stressed baseline had significant difference in the reproductive and maturation stages with an increase of 2.1 °C, however, the non-transpiring baseline did not change significantly with an increase of 0.1 °C. The ratio of transformed chlorophyll absorption in reflectance index (TCARI) and renormalized difference vegetation index (RDVI), and the TCARI and soil-adjusted vegetation index (SAVI) had the best correlations with CWSI. R2 values were 0.47 and 0.50 for TCARI/RDVI and TCARI/SAVI at the reproductive and maturation stages, respectively; and 0.81 and 0.80 for TCARI/RDVI and TCARI/SAVI at the late reproductive and maturation stages, respectively. Compared to CWSI calculated by on-site measurements, CWSI values retrieved by VI-CWSI regression models established in this study had more abilities to assess the field variability of crop and soil. This study demonstrates the potentiality of using high-resolution UAV multispectral imagery to map maize water stress.

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Section: Introductionmentioning

confidence: 99%

“…Comparison of CWSI values (at DOY 231 and DOY 240) using two different methods (1). CWSI values calculated by on-site measurements within each sampling plot, and (2) CWSI values retrieved from CWSI vs. VI regression models (Equations(14) and(15)).…”

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confidence: 99%

Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing

et al. 2019

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“…Crop models simulate crop growth and development and predict crop yields using meteorological, soil, field management, and cultivar parameters, among others [15,16]. Crop models can simulate the growth stages of crops and quantify the physiological and ecological processes [17,18]. However, crop models require many parameters, and uncertainty in the parameters often leads to uncertainty in the simulation results [19].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Recent Climate Change on Potato Yields at a Provincial Scale in Northwest China

Zhang

2020

Agronomy

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Understanding the effects of climate change on potato yield is vital for food security in northwest China. Based on the long-term data of yields and meteorology, this study analysed the impacts of recent climate change on potato yields at a provincial scale in northwest China. The first difference method was used to disentangle the contributions of climate change from the changes in potato yield in two consecutive years. The moving average method was used to decouple the climate-induced yield of potato. The results showed that the yield and planting area of potato from the period 1982 to 2015 increased markedly, with inter-annual fluctuations. The temperature increased significantly during the potato growing period in northwest China, while other climatic factors did not change significantly. Specifically, the changing trends in climatic factors varied among different provinces. The key meteorological factors limiting potato yield were temperature, precipitation and diurnal temperature range, varying in the different provinces. Potato yields in Gansu, Shaanxi, Ningxia and Xinjiang decreased by 127, 289, 199 and 339 kg ha−1, respectively, for every 1 °C increase in daily maximum temperature. The potato yield in Xinjiang decreased by 583 kg ha−1 for every 1 °C increase in daily minimum temperature. For every 100 mm increase in precipitation, the potato yields in Gansu, Qinghai and Ningxia increased by 250, 375 and 182 kg ha−1, respectively. Combining the first difference method and the moving average method, precipitation was the dominant climatic factor affecting potato yield in rain-fed areas (Gansu, Qinghai and Ningxia). For areas with irrigation (Xinjiang) or relatively high rainfall (Shaanxi), maximum temperature was the deciding climatic factor affecting potato yield. Appropriate adaptation to climate change in the different regions will help to ensure potato production in northwest China.

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“…This was in agreement with the findings of Dogan et al [13] and Foroud et al [43] by field experiments. In addition, Lopez et al [47] found that the water use efficiency with irrigation only during soybean reproductive stage R3 was 18% higher than that under the well-watered condition in Gainesville by model simulation.…”

Section: Decision-making Results Of Irrigation Scheme For Soybeans Inmentioning

confidence: 99%

Decision-Making of Irrigation Scheme for Soybeans in the Huaibei Plain Based on Grey Entropy Weight and Grey Relation–Projection Pursuit

Jiang

Jin

et al. 2019

Entropy

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To provide a scientific reference for formulating an effective soybean irrigation schedule in the Huaibei Plain, potted water deficit experiments with nine alternative irrigation schemes during the 2015 and 2016 seasons were conducted. An irrigation scheme decision-making index system was established from the aspects of crop water consumption, crop growth process and crop water use efficiency. Moreover, a grey entropy weight method and a grey relation–projection pursuit model were proposed to calculate the weight of each decision-making index. Then, nine alternative schemes were sorted according to the comprehensive grey relation degree of each scheme in the two seasons. The results showed that, when using the entropy weight method or projection pursuit model to determine index weight, it was more direct and effective to obtain the corresponding entropy value or projection eigenvalue according to the sequence of the actual study object. The decision-making results from the perspective of actual soybean growth responses at each stage for various irrigation schemes were mostly consistent in 2015 and 2016. Specifically, for an integrated target of lower water consumption and stable biomass yields, the scheme with moderate-deficit irrigation at the soybean branching stage or seedling stage and adequate irrigation at the flowering-podding and seed filling stages is relatively optimal.

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Integrating growth stage deficit irrigation into a process based crop model

Cited by 46 publications

References 45 publications

Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing

Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing

Impacts of Recent Climate Change on Potato Yields at a Provincial Scale in Northwest China

Decision-Making of Irrigation Scheme for Soybeans in the Huaibei Plain Based on Grey Entropy Weight and Grey Relation–Projection Pursuit

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