Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield

The temperature-based crop water stress index (CWSI) can accurately reflect the extent of crop water deficit. As an ideal carrier of onboard thermometers to monitor canopy temperature (Tc), center pivot irrigation systems (CPIS) have been widely used in precision irrigation. However, the determination of reliable CWSI thresholds for initiating the CPIS is still a challenge for a winter wheat–summer maize cropping system in the North China Plain (NCP). To address this problem, field experiments were carried out to investigate the effects of CWSI thresholds on grain yield (GY) and water use efficiency (WUE) of winter wheat and summer maize in the NCP. The results show that positive linear functions were fitted to the relationships between CWSI and canopy minus air temperature (Tc − Ta) (r2 > 0.695), and between crop evapotranspiration (ETc) and Tc (r2 > 0.548) for both crops. To make analysis comparable, GY and WUE data were normalized to a range of 0.0 to 1.0, corresponding the range of CWSI. With the increase in CWSI, a positive linear relationship was observed for WUE (r2 = 0.873), while a significant inverse relationship was found for the GY (r2 = 0.915) of winter wheat. Quadratic functions were fitted for both the GY (r2 = 0.856) and WUE (r2 = 0.629) of summer maize. By solving the cross values of the two GY and WUE functions for each crop, CWSI thresholds were proposed as being 0.252 for winter wheat, and 0.229 for summer maize, corresponding to a Tc − Ta threshold value of 0.925 and 0.498 °C, respectively. We conclude that farmers can achieve the dual goals of high GY and high WUE using the optimal thresholds proposed for a winter wheat–summer maize cropping system in the NCP.

[1]  N. B. Hani,et al.  The Effect of Supplemental Irrigation on Canopy Temperature Depression, Chlorophyll Content, and Water Use Efficiency in Three Wheat ( Triticum aestivum L. and T. durum Desf.) Varieties Grown in Dry Regions of Jordan , 2018 .

[2]  Chandra A. Madramootoo,et al.  Recent advances in crop water stress detection , 2017, Comput. Electron. Agric..

[3]  Alon Ben-Gal,et al.  Determining threshold values for root-soil water weighted plant water deficit index based smart irrigation , 2020 .

[4]  Y. Erdem,et al.  Crop water stress index for potato under furrow and drip irrigation systems , 2005, Potato Research.

[5]  Jianbin Xiong,et al.  Water Allocation and Integrative Management of Precision Irrigation: A Systematic Review , 2020 .

[6]  Xiaoyin Liu,et al.  Improving the performance in crop water deficit diagnosis with canopy temperature spatial distribution information measured by thermal imaging , 2021 .

[7]  E. Acevedo,et al.  Effects of Water Deficits on Prosopis tamarugo Growth, Water Status and Stomata Functioning , 2020, Plants.

[8]  Gonzalo Cucho-Padin,et al.  Development of an Open-Source Thermal Image Processing Software for Improving Irrigation Management in Potato Crops (Solanum tuberosum L.) , 2020, Sensors.

[9]  Federica Gaiotti,et al.  Infrared Thermography to Estimate Vine Water Status: Optimizing Canopy Measurements and Thermal Indices for the Varieties Merlot and Moscato in Northern Italy , 2019, Agronomy.

[10]  Xinxing Li,et al.  Precision irrigation perspectives on the sustainable water-saving of field crop production in China: Water demand prediction and irrigation scheme optimization , 2019, Journal of Cleaner Production.

[11]  Higher yield and lower carbon emission by intercropping maize with rape, pea, and wheat in arid irrigation areas , 2014, Agronomy for Sustainable Development.

[12]  Wenting Han,et al.  A Fixed-Threshold Method for Estimating Fractional Vegetation Cover of Maize under Different Levels of Water Stress , 2021, Remote. Sens..

[13]  D. Z. Haman,et al.  Determination of Crop Water Stress Index for Irrigation Timing and Yield Estimation of Corn , 2000 .

[14]  R. Quiroz,et al.  Canopy Temperature as a Key Physiological Trait to Improve Yield Prediction under Water Restrictions in Potato , 2021, Agronomy.

[15]  Aiwang Duan,et al.  Analysis of the Accuracy of an FDR Sensor in Soil Moisture Measurement under Laboratory and Field Conditions , 2021, J. Sensors.

[16]  B. King,et al.  Application of a Daily Crop Water Stress Index to Deficit Irrigate Malbec Grapevine under Semi-Arid Conditions , 2020 .

[17]  Paul D. Colaizzi,et al.  A crop water stress index and time threshold for automatic irrigation scheduling of grain sorghum , 2012 .

[18]  P. Zarco-Tejada,et al.  Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle , 2014, Precision Agriculture.

[19]  Andrea Berton,et al.  Estimation of Water Stress in Grapevines Using Proximal and Remote Sensing Methods , 2018, Remote. Sens..

[20]  Paul D. Colaizzi,et al.  Dynamic prescription maps for site-specific variable rate irrigation of cotton , 2015 .

[21]  M. Y. Roh,et al.  ENVIRONMENTAL CONTROL IN GREENHOUSE BASED ON PHYTOMONITORING - LEAF TEMPERATURE AS A FACTOR CONTROLLING GREENHOUSE ENVIRONMENTS , 2007 .

[22]  S. Irmak,et al.  Variable upper and lower crop water stress index baselines for corn and soybean , 2006, Irrigation Science.

[23]  Steven R. Evett,et al.  Automation of a Center Pivot Using the Temperature-Time- Threshold Method of Irrigation Scheduling , 2008 .

[24]  B. King,et al.  Kaolin Particle Film and Water Deficit Influence Malbec Leaf and Berry Temperature, Pigments, and Photosynthesis , 2013, American Journal of Enology and Viticulture.

[25]  Manfred Stoll,et al.  Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine. , 2002, Journal of experimental botany.

[26]  Jiangang Zhang,et al.  Effects of soil heat storage and phase shift correction on energy balance closure of paddy fields , 2017 .

[27]  Paul D. Colaizzi,et al.  WIRELESS SENSOR NETWORK EFFECTIVELY CONTROLS CENTER PIVOT IRRIGATION OF SORGHUM , 2013 .

[28]  Danielo G. Gomes,et al.  Smart & Green: An Internet-of-Things Framework for Smart Irrigation , 2019, Sensors.

[29]  A. Hoekstra,et al.  Global Gray Water Footprint and Water Pollution Levels Related to Anthropogenic Nitrogen Loads to Fresh Water. , 2015, Environmental science & technology.

[30]  M. Kacira,et al.  Establishing crop water stress index (CWSI) threshold values for early, non-contact detection of plant water stress , 2000 .

[31]  H. Jones,et al.  Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. , 2009, Functional plant biology : FPB.

[32]  A. Qin,et al.  Insentek Sensor: An Alternative to Estimate Daily Crop Evapotranspiration for Maize Plants , 2018, Water.

[33]  Zhigang Liu,et al.  Precision Irrigation Scheduling Using ECH2O Moisture Sensors for Lettuce Cultivated in a Soilless Substrate Culture , 2018 .

[34]  Narendra Kumar Gontia,et al.  Development of crop water stress index of wheat crop for scheduling irrigation using infrared thermometry , 2008 .

[35]  Shaokun Li,et al.  Grain yields and evapotranspiration dynamics of drip-irrigated maize under high plant density across arid to semi-humid climates , 2021 .

[36]  Kendall C. DeJonge,et al.  Comparison of canopy temperature-based water stress indices for maize , 2015 .

[38]  A. Qin,et al.  Structural Equation Modeling of Soil Moisture Effects on Evapotranspiration of Maize in the North China Plain , 2020 .

[39]  Inge Sandholt,et al.  Diurnal and Seasonal Mapping of Water Deficit Index and Evapotranspiration by an Unmanned Aerial System: A Case Study for Winter Wheat in Denmark , 2021, Remote. Sens..

[40]  Md. Nesar Uddin,et al.  Evaluation of the Tolerance Ability of Wheat Genotypes to Drought Stress: Dissection through Culm-Reserves Contribution and Grain Filling Physiology , 2021 .

[41]  Effects of Irrigation Management on Chipping Potato (Solanum tuberosum L.) Production in the Upper Midwest of the U.S. , 2021 .

[42]  Yubin Lan,et al.  Rapid Estimation of Crop Water Stress Index on Tomato Growth , 2021, Sensors.

[43]  Martin A. Hebel,et al.  Original papers: Evaluation of a wireless infrared thermometer with a narrow field of view , 2011 .

[44]  John Hornbuckle,et al.  Monitoring the Effects of Water Stress in Cotton Using the Green Red Vegetation Index and Red Edge Ratio , 2019, Remote. Sens..

[45]  R. Guevara-González,et al.  Differential Response to Water Deficit in Chili Pepper (Capsicum annuum L.) Growing in Two Types of Soil Under Different Irrigation Regimes , 2020, Agriculture.

[46]  P. Zarco-Tejada,et al.  Seasonal evolution of crop water stress index in grapevine varieties determined with high-resolution remote sensing thermal imagery , 2015, Irrigation Science.

[47]  A. Qin,et al.  Incorporation of Manure into Ridge and Furrow Planting System Boosts Yields of Maize by Optimizing Soil Moisture and Improving Photosynthesis , 2019, Agronomy.

[48]  Y. Cohen,et al.  Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. , 2006, Journal of experimental botany.

[49]  Ya-wei Li,et al.  Modeling rice development and field water balance using AquaCrop model under drying-wetting cycle condition in eastern China , 2019, Agricultural Water Management.

[50]  Xiaotao Hu,et al.  Evaluation of the Crop Water Stress Index as an Indicator for the Diagnosis of Grapevine Water Deficiency in Greenhouses , 2020, Horticulturae.

[51]  Juan José Alarcón Cabañero,et al.  Determination of Crop Water Stress Index by Infrared Thermometry in Grapefruit Trees Irrigated with Saline Reclaimed Water Combined with Deficit Irrigation , 2019, Remote. Sens..

[52]  E. C. Stegman,et al.  Field Comparison of Irrigation Scheduling Methods for Corn , 1994 .

[53]  E. C. Stegman Efficient Irrigation Timing Methods for Corn Production , 1986 .

[54]  Pablo J. Zarco-Tejada,et al.  Applicability and limitations of using the crop water stress index as an indicator of water deficits in citrus orchards , 2014 .

[55]  A. Ben‐Gal,et al.  Root-weighted soil water status for plant water deficit index based irrigation scheduling , 2017 .

[56]  Seyed Mahmood Kashefipour,et al.  Relationships between leaf water potential, CWSI, yield and fruit quality of sweet lime under drip irrigation , 1994 .

[57]  M. A. Jiménez-Bello,et al.  Usefulness of thermography for plant water stress detection in citrus and persimmon trees , 2013 .

[58]  Lav R. Khot,et al.  Economical thermal-RGB imaging system for monitoring agricultural crops , 2018, Comput. Electron. Agric..

[59]  L. Helyes,et al.  Physiological Responses of Selected Vegetable Crop Species to Water Stress , 2019, Agronomy.

[60]  J. W. Radin,et al.  Water Uptake by Cotton Roots during Fruit Filling in Relation to Irrigation Frequency , 1989 .

[61]  Alessandro Matese,et al.  Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture , 2018, Agriculture.

[62]  Manuel Perez-Ruiz,et al.  Assessing a crop water stress index derived from aerial thermal imaging and infrared thermometry in super-high density olive orchards , 2017 .

[63]  V. Hernandez-Santana,et al.  Assessing the Water-Stress Baselines by Thermal Imaging for Irrigation Management in Almond Plantations under Water Scarcity Conditions , 2020, Water.