Water effects on optical canopy sensing for late-season site-specific nitrogen management of maize

Abstract The interpretation of optical canopy sensor readings for determining optimal rates of late-season site-specific nitrogen application to corn (Zea mays L.) can be complicated by spatially variable water sufficiency, which can also affect canopy size and/or pigmentation. In 2017 and 2018, corn following corn and corn following soybeans were subjected to irrigation × nitrogen fertilizer treatments in west central Nebraska, USA, to induce variable water sufficiency and variable nitrogen sufficiency. The vegetation index-sensor combinations investigated were the normalized difference vegetation index (NDVI), the normalized difference red edge index (NDRE), and the reflectance ratio of near infrared minus red edge over near infrared minus red (DATT) using ACS-430 active optical sensors; NDVI using SRS-NDVI passive optical sensors; and red brightness and a proprietary index using commercial aerial visible imagery. Among these combinations, NDRE and DATT were found to be the most suitable for assessing nitrogen sufficiency within irrigation levels. While DATT was the least sensitive to variable water sufficiency, DATT still tended to decrease with decreasing water sufficiency in high nitrogen treatments, whereas the effect of water sufficiency on DATT was inconsistent in low nitrogen treatments. A new method of quantifying nitrogen sufficiency while accounting for water sufficiency was proposed and generally provided more consistent improvement over the mere averaging of water effects as compared with the canopy chlorophyll content index method. Further elucidation and better handling of water-nitrogen interactions and confounding are expected to become increasingly important as the complexity, automation, and adoption of sensor-based irrigation and nitrogen management increase.

[1]  Jeffrey W. White,et al.  Active Optical Sensors in Irrigated Durum Wheat: Nitrogen and Water Effects , 2017 .

[2]  Edward M. Barnes,et al.  Remote Sensing of Cotton Nitrogen Status Using the Canopy Chlorophyll Content Index (CCCI) , 2008 .

[3]  B. Datt,et al.  Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves , 1999 .

[4]  W. E. Larson,et al.  Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data. , 2000 .

[5]  G. Kruger,et al.  Crop canopy sensor orientation for late season nitrogen determination in corn , 2017 .

[6]  James S. Schepers,et al.  Use of a virtual-reference concept to interpret active crop canopy sensor data , 2013, Precision Agriculture.

[7]  S. Idso,et al.  Normalizing the stress-degree-day parameter for environmental variability☆ , 1981 .

[8]  W. Bausch.  Soil background effects on reflectance-based crop coefficients for corn☆ , 1993 .

[9]  Jiyul Chang,et al.  Characterizing Water and Nitrogen Stress in Corn Using Remote Sensing , 2006 .

[10]  A. Gitelson,et al.  Active Sensor Reflectance Measurements of Corn Nitrogen Status and Yield Potential , 2008 .

[11]  S. Irmak,et al.  Impact of Nitrogen Fertilizer on Maize Evapotranspiration Crop Coefficients under Fully Irrigated, Limited Irrigation, and Rainfed Settings , 2014 .

[12]  S. Idso,et al.  Canopy temperature as a crop water stress indicator , 1981 .

[13]  Daran R. Rudnick,et al.  Economic Return versus Crop Water Productivity of Maize for Various Nitrogen Rates under Full Irrigation, Limited Irrigation, and Rainfed Settings in South Central Nebraska , 2016 .

[14]  G. Fitzgerald,et al.  Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments , 2006, Precision Agriculture.

[15]  G. Vachaud,et al.  Temporal Stability of Spatially Measured Soil Water Probability Density Function , 1985 .

[16]  J. Schepers,et al.  Transmittance and reflectance measurements of corn leaves from plants with different nitrogen and water supply , 1996 .

[17]  N. U. Ahmed,et al.  Relations between evaporation coefficients and vegetation indices studied by model simulations , 1994 .

[18]  Viacheslav I. Adamchuk,et al.  Water and Nitrogen Effects on Active Canopy Sensor Vegetation Indices , 2011 .

[19]  Simon D. Jones,et al.  Remote sensing of nitrogen and water stress in wheat , 2007 .

[20]  James S. Schepers,et al.  Aerial Photography to Detect Nitrogen Stress in Corn , 1996 .

[21]  P. Scharf,et al.  Calibrating Corn Color from Aerial Photographs to Predict Sidedress Nitrogen Need , 2002 .

[22]  Vijaya Gopal Kakani,et al.  Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum , 2005 .

[23]  Yufeng Ge,et al.  A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding , 2016, Comput. Electron. Agric..

[24]  James S. Schepers,et al.  Derivation of a Variable Rate Nitrogen Application Model for In‐Season Fertilization of Corn , 2010 .

[25]  M. S. Moran,et al.  Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index , 1994 .

[26]  Andrew E. Suyker,et al.  Evaluation of a Hybrid Reflectance-Based Crop Coefficient and Energy Balance Evapotranspiration Model for Irrigation Management , 2018 .

[27]  Jeffrey W. White,et al.  Interactive effects of nitrogen fertilization and irrigation on grain yield, canopy temperature, and nitrogen use efficiency in overhead sprinkler-irrigated durum wheat. , 2016 .

[28]  Joe D. Luck,et al.  Soil water content monitoring for irrigation management:A geostatistical analysis , 2017 .

[29]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[30]  J. S. Schepers,et al.  Use of a Chlorophyll Meter to Monitor Nitrogen Status and Schedule Fertigation for Corn , 1995 .

[31]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[32]  T. Arkebauer,et al.  Reflectance-based crop coefficients REDUX: For operational evapotranspiration estimates in the age of high producing hybrid varieties , 2017 .

[33]  D. K. Biswas,et al.  Effects of nitrogen deficiency on photosynthetic traits of maize hybrids released in different years. , 2005, Annals of botany.

[34]  James S. Schepers,et al.  Measuring Chlorophyll Content in Corn Leaves with Differing Nitrogen Levels and Relative Water Content , 2019 .