Introduction: A Journey from Soil to Plant Sensing
My initial foray into soil moisture sensing began as an undergraduate, driven by the ambition to develop the "most accurate" soil moisture sensor. However, years of research have underscored the intricate complexities of this task, a project that continues to evolve.
During my academic journey, I explored various approaches to soil moisture determination, ultimately finding inspiration in the pioneering work of Jackson et al. (1981) and USDA-ARS scientists at Bushland, TX, who focused on plant-based (thermal sensing) irrigation scheduling.
This pivotal moment shifted my research focus towards plant-based water stress detection and irrigation scheduling, culminating in our work at the university (with funding from private sector). This article summarizes our efforts and outlines future research directions.
Research Objectives: Precision Irrigation Management
Our research aimed to develop and evaluate techniques for precision canopy and water management in crops through sensor-based decision-making. Specifically, we set out to:
Develop a site-specific irrigation control and monitoring system: This system would continuously monitor plant water status, determine water requirements, and automatically schedule irrigation.
Design and deploy a wireless network of soil, plant, and microclimate sensors.
Determine plant water requirements in real-time.
Develop and assess irrigation scheduling algorithms based on plant, soil, and weather data.
Create a sensor-based setup incorporating plant-based models for real-time, non-contact sensing of soil water content, soil water potential, and stem water potential, using canopy temperature and micrometeorological parameters.
Challenges in Plant-Based Irrigation Scheduling: The Case of Apple Trees
We sought to use plant sensors, rather than soil sensors, to detect plant water stress and guide irrigation decisions in apple orchards. However, the conventional Crop Water Stress Index (CWSI) presented significant challenges:
Limitations of Existing Evapotranspiration Models: Models like Penman-Monteith often fail to accurately represent stomatal regulation in tree crops, leading to unreliable estimations. While empirical and FAO-56-based theoretical CWSI models exist, the FAO-56 equations are based on alfalfa or grass, which differ significantly from apple trees in stomatal control. Apple tree leaves are highly responsive to atmospheric conditions, tightly regulating stomata to minimize water loss (Figure 1).
Lack of Precedent in Tree Crops: Automated plant-based irrigation scheduling methods were virtually unexplored in apple trees and other tree crops.
Crop-Specific Modeling: Tailoring CWSI for Apple Trees
To address these challenges, we developed a novel, crop-specific CWSI model based on apple tree physiology and our research findings. This model incorporates a canopy conductance sub-model to account for stomatal regulation and estimates average actual and potential transpiration rates for the canopy area viewed by an infrared temperature (IRT) sensor or thermal camera (Figure 2).
Our "Apple Tree" CWSI is based on the energy budget of a single apple leaf, eliminating the need for a soil heat flux component. However, the model's accuracy hinges on precise reference soil moisture measurements. To establish a relationship between CWSI and soil water, we employed a neutron probe (NP) for its precision and volume of influence, alongside continuous soil sensor monitoring (Figure 3).
Field Data Collection and Results: Validating the Model
We conducted field measurements in multiple experimental and commercial orchards across Washington State over four years. Data collection included:
Soil water deficit measurements using NP in the root zone (top 60 cm).
Canopy surface temperature data from IRT sensors.
Continuous soil water content and potential measurements from soil sensors.
Microclimate data.
Our results revealed a strong correlation between the new CWSI model and soil water deficit in apple trees across all orchards. The model demonstrated high sensitivity to mild variations in soil water content, indicating its potential as a robust indicator of root zone water availability (Figure 5).
We observed high correlations between soil water deficit, soil water potential, and thermal-based water stress indices in the mildly stressed range. However, we discovered that these relationships were time-sensitive, peaking between 10:00 am and 11:00 am, the time of maximum transpiration. The correlation significantly decreased outside this window (Figure 6). NP data yielded the strongest correlations, while soil sensor data were acceptable.
This time-sensitive behavior contrasts with conventional midday CWSI measurements and highlights the unique stomatal regulation of apple trees, which exhibit peak transpiration in the late morning and late afternoon, and stomatal closure during hot midday hours.
Comparison with Other Research: Contextualizing Our Findings
While satellite-based thermal and NIR measurements have been used to estimate soil water content, these methods cannot accurately predict root zone water status for irrigation scheduling. Studies by Colaizzi and Evett at USDA-ARS have shown promising correlations between integrated daily CWSI and soil water content in row crops at high soil water deficits.
Future Research Directions: Expanding the Scope
Future research will focus on:
Developing improved methods for real-time monitoring of large soil volumes in the root zone, to replace the labor-intensive NP method.
Assessing the universality of our equations across different fruit trees, row crops, irrigation systems, and climates.
Integrating crop health monitoring to isolate water stress signals (Figure 7).
References
Jackson, R. D., Idso, S. B., Reginato, R. J., Pinter, P. J. Jr., 1981. Canopy temperature as a crop water stress indicator. Water Resour. Res.17, 1133–1138.
Osroosh, Y., Peters, R.T., Campbell, C., Zhang, Q., 2016. Comparison of irrigation automation algorithms for drip-irrigated apple trees. Computers and Electronics in Agriculture, 128: 87–99.
Osroosh, Y., Peters, R.T., Campbell, C., 2016. Daylight crop water stress index for continuous monitoring of water status in apple trees. Irrigation Science, 34(3): 209–219.
Osroosh, Y., Peters, R.T., Campbell, C., Zhang, Q., 2015. Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold. Computers and Electronics in Agriculture, 118: 193–203.
Osroosh, Y., Peters, R.T., Campbell, C., 2015. Estimating potential transpiration of apple trees using theoretical non-water-stressed baselines. Journal of Irrigation and Drainage Engineering, 141(9): 04015009.
Osroosh, Y., Peters, R.T., Campbell, C., 2015. Estimating actual transpiration of apple trees based on infrared thermometry. Journal of Irrigation and Drainage Engineering, 141(8): 04014084.
Ferrer-Alegre, F., Mohamed, A.Z., Osroosh, Y., Bates, T., Campbell, C., Peters, R.T., 2019. A comparative study of irrigation scheduling based on morning, daylight and daily crop water stress index dynamic threshold (CWSI-DT) in apple trees. IX International Symposium on Irrigation of Horticultural Crops. June 17-20. Matera, Italy.
Mohamed, A.Z., Osroosh, Y., Peters, R.T., Bates, T., Campbell, C., Ferrer-Alegre, F., 2019. Morning crop water stress index as a sensitive indicator of water status in apple trees. ASABE Annual International Meeting. July 7-10. Boston, MA.
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