REEport for our SCRI Project: Precision canopy and water Management of Specialty Crops Through Sensor-Based Decision Making.

REEport Project Number:CA-D-BAE-2082-OG

1. Target Audience:

UC Davis:

Lightbar and Canopy Management: Demonstrations at field meetings conducted in California (for almond and walnut growers) and Oregon (for hazelnut growers) have provided outreach to growers and PCAs demonstrating the mobile platform light bar and explaining how it can be used to evaluate pruning treatments, new varieties, rootstocks, relative level of productivity, etc. Presentations have also been done at the annual Almond Board of California Conferences as well as the annual Walnut Research Conference in Bodega Bay.

Wireless Network: Researchers, nursery producers, orchard producers, equipment manufacturers, irrigation equipment companies, farmers, and policymakers. Light-interception/Potential Yield and Plant Water Stress for Precision Irrigation Management: Growers, extension specialists, farm advisers, sensor and agricultural equipment companies, and researchers. Univ. Arizona: Commercial pecan farmers in the western US production region with farming systems relying on irrigation for water management and who have adopted mechanical-hedge pruning techniques for canopy management. Washington State University:

Specialty crop producers and processers who are seeking new solutions for precision canopy and water management to improve crop production and quality are the primary audience of this project.

2. Products (Note – This project must be acknowledged in all products. Indicate if you have done so).

a. Publications (Journal articles, Conference papers, thesis/dissertation etc.):

Lightbar:

Lampinen, Bruce D., Vasu Udompetaikul, Gregory T. Browne, Samuel G. Metcalf, William L. Stewart, Loreto Contador, Claudia Negrón, and Shrini K. Upadhyaya. 2012. A mobile platform for measuring canopy

photosynthetically active radiation interception in orchard systems. HortTechnology 22: 237-244 .

Wireless Network (Mike’s group):

Coates, R., M. Delwiche, R. Evans, L. Oki, L. Schwankl. 2015. Adjustable-Rate Fertigation System for Container Nurseries. Applied Engineering in Agriculture 30(6). Coates, R.W., M.J. Delwiche, A. Broad, M. Holler. 2013. Wireless sensor network with irrigation valve control. Comput. Electron. Agric. 96:13–22. http://dx.doi.org/10.1016/j.compag.2013.04.013 Coates, R. W., P.K. Sahoo, L.J. Schwankl, and M.J. Delwiche. 2012. Fertigation techniques for use with multiple hydro-zones in simultaneous operation. Precision Agriculture 13(2):219-235. Jimenez-Berni, Miguel. 2012. Variable Rate Irrigation: Design of Control Software and Field Evaluation in California Orchards. MS Thesis. University of California, Davis, Department of Biological and Agricultural Engineering. Vineyard (Mark’s group): Hsieh, F, Hsueh C, Heitkamp C, and Matthews MA Integrative inferences on pattern geometries of grapes grown under water stress and their resulting wines. Proceedings of Royal Society. Shrini’s Group: Dhillon, R., F. Rojo, J. Roach, S. Upadhyaya and M. Delwiche. 2014. A continuous leaf monitoring system for precision irrigation management in orchard crops. J. Agr. Machinery Sci. 10(4):267-272. Dhillon, R., V. Udompetaikul, F. Rojo, J. Roach, S. Upadhyaya, D. Slaughter, B. Lampinen, and K. Shackel. 2014. Detection of plant water stress using leaf temperature and microclimatic measurements in almond, walnut, and grape crops. Trans. ASABE. 57(1):297-304. Udompetailkul, V, S. K. Upadhyaya, D. C. Slaughter, and B. D. Lampinen. 2010. Development of a sensor suite to determine plant water potential. ASABE paper. 1009450. ASABE, St. Joseph, MI 49085. Udompetailkul, V, S. K. Upadhyaya, D. C. Slaughter, and B. D. Lampinen. 2010. Development of a sensor suite to determine plant water potential. Proceedings of the 10th International Conference of Precion Agriculture, Paper No. 10-391. July 18-21, Denver, CO. USA. Udompetailkul, V, S. K. Upadhyaya, D. C. Slaughter, B. D. Lampinen, and K. Shackel. 2011. Plant water stress dection using leaf temperature and microclimatic information. ASABE paper 111555. ASABE, St. Joseph, MI 49085. Dhillon, R., V. Udompetaikul, F. Rojo, S. Upadhyaya, and D. Slaughter. 2012. Evaluation of a sensor suite for dection of plant watewr stress in orchard and vineyard crops. 11th International Conference of Precion Agriculture, Indianapolis, IN. July 15 -18, 2012.

Dhillon, R., V. Udompetaikul, F. Rojo, S. Upadhyaya, J. Roach, and D. Slaughter, B. Lampinen, K. Shackel. 2012. Detection of plant water stress using leaf temperature for vineyard and nut crops. ASABE paper no. 121338089. ASABE, St. Joseph, MI 49085 (Accepted for publications in Trans. ASABE). Dhillon, R., F. Rojo, J. Roach, R. Coates, S. Upadhyaya and M. Delwiche. 2013. Development and evaluation of a leaf monitoring system for continous mesurement of plant water status. ASABE paper no. 131338089. ASABE, St. Joseph, MI 49085. Rojo,F. R. Dhillon, J. Roach, S. Upadhyaya, B. Lampinen, S. Metcalf, and H. Changjie. 2013. Sensing light absorption by crop canopy for estimating yield in almonds and orchards. ASABE paper no. 131338089. ASABE, St. Joseph, MI 49085. Dhillon, R. S., F. Rojo, J. Roach, and S. Upadhyaya. 2014. Handheld sensor suite for plant water status measurements and a comparison of different techniques to measure canopy temperature in orchard crops. ASABE paper 141893976. ASABE St. Joseph, MI 49085. Rojo, F., R. S. Dhillon, S. Upadhyaya, J. Roach, K. Crawford, B. Lampinen, and S. Metcalf. 2014. Modeling canopy light interception for estimating potential yield in almond and walnut trees. ASABE paper 141896144. ASABE St. Joseph, MI 49085. Crawford, K., J. Roach, R. Dhillon, F. Rojo., and S. K. Upadhyaya. 2014. An inexpensive aerial platform for precise remote sensing of almond and walnut canopy temperature. A paper presented at the 12th International Conference on Precision Agriculture in Sacramento, CA, USA. July 20-23. Rojo, F., R. Dhillon, S. Upadhyaya, B. Jenkins., B. Lampinen, J. Roach, K. Crawford, and S. Metcalf. 2014. Modeling canopy light interception for estimating yield in almond and walnut trees. A paper presented at the 12th International Conference on Precision Agriculture in Sacramento, CA, USA. July 20-23. Dhillon, R., F. Rojo., J. Roach., R. Coates, S. K. Upadhyaya, M. Delwiche, and C. Han. 2014. Development and evaluation of a leaf monitoring system for continuous measurement of plant water status in almond and walnut crops. A paper presented at the 12th International Conference on Precision Agriculture in Sacramento, CA, USA. July 20-23. Udompetaikul, V. 2012. Development of a sensor suite for plant water status determination for irrigation management in specialty crops. Unpublished PhD dissertation, Bio. And Agr. Eng. Dept., Univ. Cal. Davis. Davis, CA 95616. 178 pp. Dhillon, R. 2014. Development and evaluation of a continous Leaf Monitoring System for measurement of plant water stress. Unpublished PhD dissertation, Bio. And Agr. Eng. Dept., Univ. Cal. Davis. Davis, CA 95616. 473pp. Crawford, K. 2014. Remote sensing of almond and walnut tree canopy temperature using an inexpensive infrared sensor mounted on a small unmmaned unmanned vehicle. Unpublished MS thesis, Bio. And Agr.

Eng. Dept., Univ. Cal. Davis. Davis, CA 95616. 245pp. Rojo, F. E. 2015. Modeling canopy PAR interception for estimating potential yield in almond and walnut trees. Unpublished PhD dissertation, Bio. And Agr. Eng. Dept., Univ. Cal. Davis. Davis, CA 95616. 155pp. Washington State University: Li, L., T. Peters, Q. Zhang, J. Zhang, and D. Huang, (2014). Modeling apple surface temperature dynamics based on weather data. Sensors, 14:20217-20234. Li, L., Q. Zhang, and D. Huang, (2014). A review of imaging technique for plant phenotyping. Sensors. 14: 20078-20111 Shao, Y., L. Tan, B. Zeng, and Q. Zhang, (2014). Canopy pruning grade classification based on fast Fourier transform and artificial neural network. Transactions of the ASABE. 57(3), 963-971. Osroosh, Y., R.T. Peters, and C.S. Campbell. 2015. Estimating actual transpiration of apple trees based on infrared thermometry. Journal of Irrigation and Drainage Engineering, 10.1061/(ASCE) IR.1943-4774.0000860, 04014084 Zhang, J., M.D. Whiting, and Q. Zhang, (2015). Diurnal pattern in canopy light interception for tree fruit orchard trained to an upright fruiting offshoots (UFO) architecture. Biosystems Engineering, 129(1): 1-10. Zhang, J., Q. Zhang, and M.D. Whiting, (2015). Mapping interception of photosynthetically active radiation in sweet cherry orchards. Computers and Electronics in Agriculture. 111: 29-37. Osroosh, Y., R.T. Peters, and C.S. Campbell, (2015). Estimating potential transpiration of apple trees using theoretical non-water-stressed baselines. Accepted for publication in the Journal of Irrigation and Drainage Engineering. Osroosh, Y., R.T. Peters, and C.S. Campbell, (2015). Infrared thermometry and microclimatic measurements in a fully irrigated apple orchard. Submitted to the Journal of Irrigation and Drainage Engineering. Zhang, J., Q. Zhang, M.D. Whiting (2014). Canopy light interception conversion in upright fruiting offshoots (UFO) sweet cherry orchard. Submitted to the Transaction of the ASABE. Conference Papers: Zhang, J., Q. Zhang, and M.D. Whiting, (2014). Canopy light interception conversion in upright fruiting offshoots (UFO) sweet cherry orchard. ASABE Paper No. 14-1893774 St. Joseph, Mich.: ASABE. Zhang, J., M.D. Whiting, and Q. Zhang, (2013). Sensor-based canopy mapping using photosynthetically active radiation interception in Y-trellis tree fruit orchards. ASABE Paper No. 13-1596279 St. Joseph, Mich.: ASABE. Zhang, J., R. Luo, P. Scharf, M.D. Whiting, and Q. Zhang, (2012). Canopy architecture affects light interception in sweet cherry. ASABE Paper No. 12-1337448 St. Joseph, Mich.: ASABE.

Tan, L., R. Haley, R. Wortman, and Q. Zhang, (2012). "An extensible and integrated software architecture for data analysis and visualization in precision agriculture. In: Proceedings of the 2012 IEEE Information Reuse and Integration (IEEE IRI'12). Las Vegas, NV. August, 2012. Dissertation: Zhang, J., (2014). Development and Application of a Novel System for Measuring Canopy Light Interception in Planar Orchards. Ph.D. Dissertation, Washington State University. Osroosh, Y., (2014). Determining Water Requirements and Scheduling Irrigation of Apple Trees Using Soil-based, Plant-based and Weather-based Methods. Ph.D. Dissertation, Washington State University. b. Patents

Upadhyaya, S. K., R. Dhillon, J. Roach, and F. Rojo. 2014. System and methods for monitoring leaf temperature for prediction of plant water stress. US 2014/0326801 A1.

c. Other Products.

Washington State University:

A Cloud-based decision support and visualization system has been developed in WSU and demonstrated to various stakeholders. There is a commercialization intent under discussion.

3. Accomplishments (Please list this by project objectives):

Objective #1: Measure Canopy Architecture and Par Absorption UC Davis: A knowledge of spatio-temporal variability in potential yield is essential for site-specific nutrient management in crop production. The objectives of this project were: (i) to develop a model for photosynthetically active radiation (PAR) intercepted by almond and walnut trees based on data obtained from respective tree(s), (ii) estimate potential crop yield in individual or a block of five trees, and (iii) to analyze if the area of the shadows and light interception at any time can be obtained from aerial images. This project used proximally sensed PAR interception data measured using a lightbar mounted on a mobile platform (Kawasaki mule), aerial images obtained from an unmanned aerial vehicle and a crop growth model to estimate potential crop yield of almond and walnut trees. Empirical and analytical models were developed to estimate PAR intercepted by almond and walnut trees. Diurnal scans were collected during the 2012 growing season and were used to validate the model. Solar noon scans were collected in Nickels Soil Laboratory, Arbuckle, CA during the 2013 and 2014 growing seasons to estimate potential yield. Diurnal scans were used to develop an empirical model, where the total amount of PAR intercepted by a tree at any time during the day, was found to be a function of zenith angle and midday PAR intercepted. The latter can be measured using the

Lightbar system or can be estimated using seasonal growth curve. Midday PAR intercepted was found to increase only during the 2012 season. The model was validated and coefficient of determination values were found to be 0.85 and 0.81 for almond and walnut trees, respectively. An analytical model was also developed for light interception by a tree canopy, where canopy was assumed to be spheroidal in shape. PAR intercepted by a tree was estimated taking into account the effect of row spacing, tree spacing within the row, latitude and longitude of the orchard, day of the year and row orientation. The model was validated with experimental data and coefficient of determination values of 0.86 and 0.94 were obtained for almond and walnut trees, respectively. Our results showed that the total amount of PAR intercepted by the tree at any time during the day can be found analytically using estimated tree canopy radius and optical density obtained using just one lightbar scan as a reference. A comparison between PAR interception data estimated analytically and empirically showed that both approaches had a similar behavior over the season. Both techniques predicted lower values of PAR intercepted in the early morning and late in the afternoon hours and peak values just before and after solar noon with a slight dip at solar noon. Good correlations were also found between yield (for both actual and potential) and absolute midday PAR intercepted for both almond and walnut trees. Absolute values of midday PAR interception better predicted yield (for both actual and potential) than the relative values of midday PAR interception. The coefficient of determination value between actual yield and absolute midday PAR intercepted was 0.70 for both crops over all three growing seasons. The coefficient of determination values ( ) for the relationship between actual and potential yield were 0.62 and 0.59 for almond and walnut, crops respectively. Moreover, a quadratic behavior was found for the relationship between potential yield and absolute midday PAR interception. The area of canopy’s shadow was found to be correlated with PAR interception and the Zenith angle. Coefficient of determination values of 0.92 and 0.88 were found for the relationship between measured and estimated values of PAR intercepted for almond and walnut trees, respectively. Furthermore, the shadow’s area at any time was found to be correlated with the area of the canopy extracted from an aerial image and the Zenith angle. Coefficient of determination values of 0.81 and 0.89 were found for the relationship between measured and estimated values of shadow’s area for almond and walnut trees, respectively. Additionally, PAR intercepted was found to be related to the canopy volume measured by a LIDAR system. Coefficient of determination values of 0.75 and 0.77 were found for block of five almond and walnut trees between the optical volume derived from PAR interception by the canopy and volume measured by the LIDAR system, respectively. In a separate study, the Kawasaki mule retrofitted with the light bar was used to collect canopy light interception data every year throughout the state in almonds, walnuts, and tree fruit crops to develop a data bank. These data were used to study the effect of pruning (canopy management), crop variety, fumigation treatment on potential yield.

Washington State University: A mobile measurement system was developed for conducting all field data collection. This system has a capacity of maintaining a constant spatial resolution as 0.01 m·s-1 (along traveling direction, maximum resolution limited by light-bar physical dimension) within a traveling speed of the platform ranging between 0 to 3.8 km·h-1. Canopy light interception on an orchard-basis was influenced by the sun position. A light interception normalization method was developed to standardize canopy light interception measured at different times and/or different locations. This method was evaluated in UFO tree orchard, with overall root mean square error (RMSE) 0.02, mean absolute percentage error (MAPE) 5.37%, and the Willmott degree of agreement (D) 0.94. Normalizing the light interception measured at such times with large canopy projection area to the value at the time with small canopy projection area was recommended. This canopy light interception normalization method could be applied on other canopy architecture by appropriate modification on the normalization index. A canopy mapping and correction method was developed to map canopy projection and to correct the distortion caused by the sun zenith and azimuth angle differences attributing to the changes in data collection time and location longitude. This method was validated by field tests of Y-trellis architecture. It could be applied on other canopy architectures as long as the relationship of the actual canopy position and its projection position can be mathematically describable. A modified Gaussian process regression method for describing the daily light interception pattern of UFO (upright fruiting offshoots) tree orchard was also developed and a representative measurement time for light interception on an orchard-basis was obtained. The effects of different canopy development stages (early fruiting, canopy development, and full canopy) and different ratios of canopy height to inter-row spacing (0.75, 1.00, and 1.25) on representative measurement time were studied. For both cases, no substantial influence was found, and measurement time 10:00 h±0.5 h was recommended. The study of dormant pruning severity detection by canopy light interception in Y-trellis orchard is ongoing. With the agreement of three experienced grower, three pruning levels were adopted, low, medium, and severe. Canopy wood area was measured before pruning, and pruned wood area was measured as well. Light interception measurement was conducted before and after pruning. University of Arizona: UA research team completed third and final year of collecting canopy light interception data from pruning studies in which mature pecan orchards in New Mexico were mechanically pruned at varying frequencies (four treatments: annual pruning, alternate pruning, pruning every 3rd year, unpruned control). In the previous seasons, weather and time allowed only a single pass through each of the treatments, but in 2014 conditions were ideal for measurements and three full passes were made during the morning hours. Light interception data have now been partially processed (i.e., cleaned up) for statistical analyses and a preliminary consultation with a New Mexico State University

statistician has been made to begin the process of analyzing the huge data set for final publication of the results in a peer reviewed journal. UA team also carried out light interception measurements in orchard property of grower cooperator FICO in Green Valley, AZ. The original orchard was divided into two parts with orthogonal tree-line directions (N-S and E-W). We have secured three years of light interception data segregated by tree orientation for further analysis. UA research team arranged a meeting between Drs. Richard Heerema, Andrade-Sanchez and James Walworth in Tucson to create plans for publication of results in research journals and extension outlets (including trade magazines). Objective #2: Detect Soil and Plant Water Status UC Davis: Limited water resources, increasing population, and environmental concerns, are making it necessary to move toward sustainable agriculture practices. Recent drought situations have highlighted the need for technologies allowing farmers to use available water resources efficiently, and to produce more food for every drop of water. Producers must apply just the right amount of water to the right plant or group of plants at the right time, and this requires implementation of novel precision irrigation techniques to improve water use efficiency. In order to deliver water according to the need of the crops, measuring or estimating water stress of the plants themselves is the most important step for developing efficient precision irrigation techniques. Historically, soil moisture measurements have been used to make irrigation decisions, but soil moisture measurements tell only the availability of water at a specific point in the root zone. To explore the spatial and temporal variability of plant water stress, sensor systems are required to predict plant water status. The sensor systems to be developed must be economically feasible, easy to automate, and convenient to use to improve on past practices. In this study, an inexpensive leaf monitoring system was developed for monitoring plant water status, and the system was evaluated for remote data collection and precision irrigation management. This system, named the “Leaf Monitor”, monitored plant water status by continuously measuring leaf temperature and other microclimatic parameters in the vicinity of the leaf. It consisted of a thermal infra-red sensor to measure leaf temperature, and sensors to measure environmental conditions such as air temperature and relative humidity, photosynthetically active radiation (PAR) and wind speed. The sensor system also consisted of a leaf holder, a solar radiation diffuser dome, and a wind barrier for improved performance of the unit. Each leaf monitor system was incorporated into a mesh network of wireless nodes, allowing data collection and transmission at 16 minute intervals over the web. Experiments were conducted during the growing seasons of 2013 and 2014 in commercial walnut and almond orchards. The wireless nodes were found to be a reliable source for providing power and transmitting data. The wireless mesh network consisted of leaf monitors, soil moisture sensors, line pressure sensors, and an actuator that could control timing and duration of water application. The network was tested in field conditions for its ability to continuously monitor the leaf by logging leaf temperature, air temperature, relative

humidity, wind speed, PAR, and implement precision irrigation management over two growing seasons. The system was found to work well and we were able to visualize all data as well as manage irrigation with minimal technical problems. A Crop Water Stress Index (CWSI) and Modified Crop Water Stress Index (MCWSI) for quantifying water stress levels were developed using the Leaf Monitor data. To account for spatial variability in plant water stress level, three different management zones were developed. Tree-specific/zone-specific stress indices were calculated using a method developed for the Leaf Monitor data. Temporal variability in stress index was accounted for by adjusting the stress calculation algorithm after every irrigation over the season. We found that adjusting the stress index calculation temporally (i.e., irrigation specific) tends to improve sensitivity for detecting changes in plant water stress. MCWSI values were found to be highly correlated with measured plant water stress. Relationships between Deficit Stem Water Potential (DSWP) and MCWSI were developed for both crops based on data collected in two seasons. A linear relationship was found for the walnut crop, with R2 = 0.68. A quadratic relationship was found for the almond crop with R2 = 0.76. The relationships showed almond trees to be more tolerant to water stress compared with walnut trees. The relationship between MCWSI and DSWP was used to implement variable rate irrigation. Pre-irrigation, MCWSI was used to calculate the irrigation prescription for low frequency irrigation in walnuts. Preliminary analysis showed 92% accuracy in making effective irrigation management decisions. On an average, 40% less water was used for variable rate irrigation (compared with 100% evapotranspiration replacement). In summary, the Leaf Monitor has the potential to be used as an irrigation scheduling tool, as it was able to provide daily stress index values that correlated well with traditional plant water stress measurements. Sensing Canopy Temperature Using a UAV: Studies at UC Davis have shown that he difference between leaf temperature and air temperature, when adjusted for environmental conditions, can give a good indication of plant water stress status. The goal of this study was to explore the feasibility of using an inexpensive temperature sensor (Melexis MLX90614; NV Melexis SA, Rozendaalstraat 12, 8900 Ieper, Belgium) on a small UAV (Mikrokopter OktoXL; Hisystems GmbH Flachsmeerstrasse 2, 26802 Moormerland, Germany) to sense the canopy temperatures of almond and walnut trees. To accomplish this goal, we installed an infrared temperature sensor and a digital camera on a small UAV. The camera provided a spatial awareness of the IR temperature measurements which would otherwise require a very expensive thermal imager to obtain. The UAV was flown above almond and walnut trees recording images and temperatures, which were aligned temporally in post-processing. The pixels of each image were classified in to four classes: sunlit leaves, shaded leaves, sunlit soil, and shaded soil. Assuming that the measured temperature could be described as a weighted sum of each class in the field of view of the IR sensor, a linear system of equations was established to estimate the temperature of each class using at least several measurements of the same tree. Results indicated a good correlation between the temperatures estimated from the linear system of equations and the temperatures of those classes

sampled on the ground immediately following each flight. With leaf temperatures ranging from about 12 to 40 degrees Celsius between 23 flights over two years, the linear solver was able to estimate the temperature of the sunlit and shaded leaves to within several degrees Celsius of the sampled temperature in most cases, with a coefficient of determination (r2 value) of 0.96 during the first year, and 0.73 during the second year.

An additional study was undertaken to detect spatial temperature distribution within the orchard. Ground measurements were taken on every other tree in two walnut rows and one almond row using the handheld sensor, and the UAV was flown over those rows immediately following each ground sampling. An interpolated temperature map of the UAV’s temperature measurements indicated a very similar temperature distribution as that measured with the handheld sensor, but the UAV was much faster and, in parts of the rows, it provided a higher spatial resolution than the handheld sensor. Vineyard: In order to evaluate the role of seasonal water deficits in productivity and wine quality, six irrigation regimes were established in a commercial Cabernet Sauvignon vineyard (Dunnigan Hills AVA in Yolo County, CA). Three treatments were maintained at constant leaf water potential targets: well-watered Control (CTL, -10 bars), grower control (RHP, -13 bars), and minimal irrigation (ED-, -14.5 bars). Two more “early deficit” treatments received no water until veraison when different rates of water were applied, namely ED (-14.5/-11 bars) and ED+ (-14.5/>-10 bars). A “late” deficit regime was well watered early in the season, with deficits imposed exclusively post-veraison (-11/-14.5 bars). Differences in canopy development resulting from water availability were especially apparent in 2012.

Grapes were harvested when treatments reached a 24 Brix target. Fruit maturity was greatly delayed by high vine water status late in the season in the ED+ treatment. Triplicate fermentations of each treatment were performed at the UC Davis Pilot Winery. Composition of grapes and wines were analyzed as well as submitting wines to a full descriptive analysis sensory study. Wines only differed significantly in astringency and “hot” mouthfeel (2012) or astringency and sour taste (2013), all of which clearly followed the concentrations established by the grape and wine analyses. When analyzing both vintages, a total of 9 attributes returned significant differences mostly driven by seasonality, thus insufficiently characterizing treatment effects other than astringency and alcohol.

Multiple datasets of two consecutive vintages of replicated grape and wine analyses from sixirrigation regimes were characterized and compared. The process consisted of four temporal-ordered signature phases: harvest yield data, juice composition, wine composition before bottling and bottled wine. A new integrative computing paradigm and an inferential platform are developed for discovering phase-to-phase pattern geometries for such characterization and comparison purposes. The partial coupling geometries of the wines at bottling and later bottled wine were similar to coupling geometry for the juice each season. This empirical fact indicates that the vintage-factor of juice is a significant causal factor for the coupling geometries of wine.

Washington State University (WSU):

WSU has a working sensor suite for remote detection of crop water stress. These sensors include a sonic anemometer, an infrared temperature sensor, a photosynthetically active radiation (PAR) sensor, a pyranometer, a leaf porometer, and a pressure bomb for stem water potential readings. Data was collected from these sensors throughout the 2013 season and this data was used to evaluate different plant water stress indicators. Specifically we tested the ability of a these remotely sensed data to predict stomotal conductance that could be used as an indicator of plant water stress. We also used this data to develop a revised method of implementing the well-studied crop water stress index (CWSI) that takes into account some of the variability of the measurements and gives a better indication of when the plants should be irrigated. Two different peer reviewed manuscripts are being prepared based on these results.

Univ. Arizona:

UA research team completed in-season measurements of soil moisture, ambient conditions, and canopy temperature in San Simon, AZ with FICO grower cooperator. High temporal density measurements were recorded on-site and transmitted to the Precision Ag laboratory at the Maricopa Agricultural Center in Maricopa AZ. Two seasons of daily measurements and final yield, both quantity and quality have been compiled for analysis on a spatial basis based on soil type. In the first year of study UA research team worked with Veris Technologies to develop a mobile, field deployable, soil moisture sensor. A dielectric based soil moisture sensor developed by Retrokool Inc., was tested by UA research team. Veris Technologies investigated the possibility of integrating an optical soil moisture sensor with a standard cone penetrometer for measuring both soil cone index and moisture content simultaneously. Although these soil moisture sensing systems showed promising results, this line of research was discontinued as pant water status rather than soil moisture content was found to be important for managing irrigation in orchard crops. Commercially available soil moisture sensors were used to record soil moisture status (i.e., not for decision making) during the subsequent years. Objective #3: Develop a Universal Navigation Computer: Trimble Navigation Ltd.:Trimble Navigation Ltd. has released a software update for its Universal Navigation Computer (UNC) under the product name FMX and released a new hardware platform named TMX-2050. These units are equipped with enhanced sensitivity GNSS (GPS + GLONASS) receivers and obtain geo-referenced sensor data under foliage where Global Positioning System (GPS) signal tends to be inaccurate. The TMX-2050 uses the Android OS and is capable of running Android Apps to streamline the development of new user capabilities which require an enhanced GNSS receiver that will work under heavy orchard canopies.

Objective #4: Develop a Visualization and Decision Support System:

Washington State University: We have successfully developed a visualization and decision support system, with the following notable features (1) cloud-based deployment - The system is developed and deployed using cloud computing technology, which improved the scalability and maintainability of the software. It enables us to quickly adjust computational resource for variations of data volume in peak and off-peak seasons; (2) visualizing data using a combination of 2D and 3D visualization technology; (3) importing real-time data collected from various sensor platforms. Users may specify the semantics and format of data, making the software capable for handling data from other existing or future sensor platforms; (4) mobile delivery. The cloud-based system has a mobile friendly web interface optimized for use with tablet computers. The system has been deployed on Amazon web services (AWS), a leading cloud computing platform.

Objective #5: Variable rate water Application System:

UC Davis: Wireless Network: Conventional agriculture has, for centuries, been applying uniform treatments (e.g., irrigation, fertilization). However, there exists spatio-temporal variability in the field that should be considered since it can result in significant differences in the management of neighboring trees or plots (i.e., blocks of trees). To improve water use efficiency it is necessary to know the environment where plants are growing, so the grower can know how much water to apply in every irrigation event. One of the main problems in the management of irrigation is that growers tend to over-irrigate, wasting water that is lost in runoff or deep percolation. The amount of water that a plant needs or transpires is closely related to the weather conditions in the area where that plant grows. In addition to irrigation management based on plant water stress, we evaluated irrigation management based on publicly-available evapotranspiration data. For both type of management, a sensing and control platform was required in order to collect and store data and allow frequent and simple changes in irrigation duration over time. For that purpose, a wireless sensor network was designed and deployed in almond and walnut tree orchards in Arbuckle, California. A valve control system was developed and integrated with the eKo Pro wireless crop monitoring system (MEMSIC, Andover, Massachusetts). The system consisted of battery-powered and solar-recharged nodes (radios) that transmit data to a base radio and computer using a mesh-network topology. A mesh network allows messages to “hop” between nodes in order to extend the range and reliability of communication. The eKo nodes could be connected to a variety of sensors including soil moisture, water pressure, water meters, and custom devices for research. The leaf monitor sensor suite discussed in objective 2 was adapted for use with the eKo wireless network. The leaf monitor was programmed to communicate with an eKo node over its RS-485 serial port. Data from pressure sensors, soil moisture sensors and leaf monitors were sent to a gateway (base radio) every 16 minutes. The data were then available as charts or tables over the web. A wireless network of sensors and actuators was deployed in almond and walnut orchards at Nickel Soil Lab, Arbuckle, CA during the 2012 growing season. The system consists of wireless nodes connected to actuator boards that control

irrigation valves. The control was done with the software developed by sending opening commands to the valves according to the water requirements for the strategy where those valves were attached. Soil moisture sensors were installed in each controlled unit and in several places in the grower orchard to monitor the water content in the soil. Pressure sensors were installed to monitor water pressure in the line for fault detection, and to know when the grower performed irrigation events. The software together with the wireless network was able to control irrigation times of 20 different irrigation management units in 36 irrigation control units. Evapotranspiration (ET-based) and stem water potential (SWP-based) were the two different strategies evaluated as the basis for calculation of the water needs of the plants. Also the grower irrigation in the rest of the orchard was monitored as a third strategy to compare. Tests were carried out in almonds and walnuts to evaluate any significant differences in water use, yield, quality, and cost between these three treatments. The irrigation between the different strategies showed significant differences in water applied in almonds and walnuts. In both crops the ET-based strategy used the least water and the grower used the most water. Short term response in the plants was done by monitoring the stem water potential. In walnuts the SWP-based strategy showed less stressed trees and less fluctuations in SWP, which makes variable rate irrigation an effective method to control plant response within heterogeneity. Yield was not significantly affected with the differences in irrigation volumes. However, significant differences were observed in mold and shrivel infestation in almonds. A possible relationship between the volume of irrigation and mold infestation could have implications for food safety. When the economic impact of different strategies was analyzed, it was observed that at the actual prices of agricultural irrigation water in California, a slight increase in yield will compensate for the irrigation cost. This issue will complicate the adoption of this technology by growers unless they have water supply restrictions which makes water use optimization a necessity. Additional tests were conducted in 2013 and 2014 cropping seasons during which the leaf monitors developed in objective #2 were fully integrated with the wireless mesh network and irrigation management was implemented after creating management zones as described under objective #2. Wireless Network Advancement: A next-generation wireless network is under development. There are three primary goals for the new system: 1) reduce the cost of an agricultural monitoring and control network to about half of current systems, 2) promote hardware and software customization by using an open source architecture, and 3) design the system to allow easy manufacturing as a commercial product. An affordable and customizable solution will allow more rapid adoption of precision agriculture techniques. We have already developed working prototypes of a complete system. The wireless radios are commercially available, have a robust mesh networking software, exhibit good range (2 miles line of sight), and are relatively inexpensive. By using commercial radios, we avoid the time and expense of radio system design and certification. The node architecture is based on an open-source hardware and software platform that allows third parties to use a familiar tool for easy

customization. Data are stored on a low cost device running a Linux operating system and are also forwarded to “cloud” servers for fast remote access. The system is compatible with several existing cloud platforms for data storage and management. The wireless network has been tested by early adopters to provide feedback for engineering improvements. Cooperators were also selected for new field trials. Demonstration systems will be installed in 4 commercial crops: alfalfa, orchard, vineyard, and container nursery. The goal is for the system to be easy to use, robust, and low cost so that it will be adopted by growers.

Washington State University: We have a working wireless network that can control a network of 21 different solenoid valves to individually control irrigation to different areas of an apple orchard. This operated throughout 2013. This system could take inputs from soil moisture sensors, air temperature sensors, canopy temperature sensors, and use this with data collected from a nearby weather station to completely automate the irrigation water management based on seven different automation methods. We evaluated the effectiveness of each of these different methods for being simple and inexpensive for a grower to implement, yet accurate enough to improve on existing irrigation scheduling practices. A separate peer reviewed manuscript is being prepared based on these results.

Objective #6: Economic Analysis (UC Davis, Oregon State University, University of Arizona):

Economic Feasibility of the Stem Water Potential Irrigation System: The cost of the stem water potential irrigation system was estimated for a 40 acre block of orchard trees under various assumptions related to the number of trees per acre and the number of nodes per acre. The system analyzed consists of a Gateway computer and a base node that serves up to 156 nodes. Each node is connected to one valve actuator/controller, one leaf monitor, and one soil moisture sensor. The valve actuators can control up to 4 valves but for this system they will each control only one valve. The annual cost for each item is calculated by amortizing the cost for 50 nodes over the years of life with an annual interest rate of four percent. The cost for a system including 50 nodes is given in Table 1. The number of nodes per acre depends on the number of trees per acre and the number of trees per node. For almond orchards in California, the tree density ranges from 75 to 175 trees per acre. Older orchards tend to have fewer trees per acre and newer orchards have higher densities. The number of nodes per acre at varying tree densities and trees per node is shown in table 3. For example, two nodes per acre are required for an orchard with 150 trees per acre and 75 trees per node. The number of nodes required for a 40 acre block at varying assumptions for tree density and trees per node is given in Table 4. Following the same example, 80 nodes are required for 40 acres. The single Gateway and base node will serve all 80 nodes.

Table 1. Hardware Costs for a SWP System with 50 Nodes

Hardware Number $/unit $/ 50 nodes Years of life

Annual cost

Nodes: 50 $400 $20,000 10 $2,465.82 Node battery replacement: 50 $50 $2,500 5 $561.57 Base node: 1 $450 $450 10 $55.48 Sensor - soil moisture: 50 $50 $2,500 5 $561.57 Sensor – leaf: 50 $250 $12,500 5 $2,807.84 Valve actuator: 50 $440 $22,000 10 $2,712.40 Valves: 50 $50 $2,500 10 $308.23 Gateway: 1 $1000 $1,000 5 $224.63 Gateway and basenode: $1,450 $280.11 Nodes, sensors, valves, actuator: $62,000 $9,417.42 Total investment: $63,450 $9,697.53

The annual operating cost of the system includes cloud storage for data generated from the sensors, irrigation labor for checking the workings of the valves from time to time, and electricity for the base station. The cloud storage cost is assumed to be $6 per month for 20 nodes. These costs are shown in Table 2. Table 2. Annual Operating Costs for SWP – 50 Nodes

Item Annual Cost

Irrigation labor (10 hours @ $15 per hour)

$150

Electricity for base station $571

Cloud Storage $180

Total annual cost $901

The annual cost of the system for 50 nodes is calculated by adding together the amortized annual investment in hardware for 50 nodes (Table 1) and the annual operating costs (Table 2). The cost for 40 acres depends on the number of nodes required. For example, for a block with a planting density of 125 trees per acre and an irrigation system with 50 trees per node, a 40 acre block would require 100 nodes (Table 4). Remember that the Gateway and base node can serve up to 156 nodes. Therefore the cost of the Gateway and base node, $280.11 per year (Table

1) will serve all 40 acres. The remaining hardware costs for the nodes, batteries, valves, actuator and sensors are $9,417 per year for 50 nodes (Table 1). The annual operating cost for 50 nodes is $901 per year (Table 2). Therefore, the annual cost for a 40 acre block requiring 100 nodes (2 times 50 nodes) equals $280.11 + 2*$9,417.42 + 2*$901 = $20,917. On a per acre basis this equals $523 per acre. The costs on a per block basis are shown in Table 5 and on a per acre basis in Table 6.

Table 3. Nodes per Acre Required at Varying Tree- Planting Density and Trees per Node

Trees per Node

Trees/Acre 50 75 100 150 175

Nodes per Acre

75 1.5 1.0 0.8 0.5 0.4

100 2.0 1.3 1.0 0.7 0.6

125 2.5 1.7 1.3 0.8 0.7

150 3.0 2.0 1.5 1.0 0.9

175 3.5 2.3 1.8 1.2 1.0

Table 4. Nodes Required for 40 Acres at Varying Density and Trees per Node

Trees per Node

Trees/Acre 50 75 100 150 175

Nodes per 40 Acre Block

75 60.0 40.0 30.0 20.0 17.1

100 80.0 53.3 40.0 26.7 22.9

125 100.0 66.7 50.0 33.3 28.6

150 120.0 80.0 60.0 40.0 34.3

175 140.0 93.3 70.0 46.7 40.0

Table 5. SWP System Cost for a 40 Acre Block at Varying Density and Trees per Node

Trees/Node Trees/Acre 50 75 100 150 175 $ for a 40 Acre Block

75 12,663 8,535 6,471

4,408

3,818

100 16,790

11,287 8,535

5,783

4,997

125 20,917

14,038

10,599

7,159

6,176

150 25,045

16,790

12,663

8,535

7,356

175 29,172

19,542

14,726

9,911

8,535

Table 6. Cost per Acre at Varying Density and Trees per Node

Trees per Node

Trees/Acre 50 75 100 150 175

$ per Acre 75 317 213 162 110 95 100 420 282 213 145 125125 523 351 265 179 154150 626 420 317 213 184175 729 489 368 248 213

Our research indicates that there is improvement in water use efficiency with across the board reduction in water application (i.e., deficit irrigation). However, while such an approach can certainly reduce water consumption, there could be adverse effect on yield. If an agronomic basis for irrigation at various stages of growth is established, this sensor based irrigation management technique has the potential to reduce water use while minimally impacting yield. In the current drought situation in California, even a slight water savings would be significant to growers facing cuts in delivery of surface water. Other known benefits include the convenience of seeing accurate plant water status without going to the field and using a pressure bomb for the measurement and the associated cost savings from not using a pressure bomb. Another important benefit is the convenience of turning irrigation water on and off remotely.

Objective #7: Social Implications:

AgInformatics: The U.S. specialty crop industry is confronted with challenges of increased competition in global markets, mounting environmental pressures and especially those concerning water, food safety, and greater uncertainty regarding the future availability of labor. Growers, production associations and private sector firms have all suggested or developed an array of strategies to address these challenges. One common dimension found in many of these strategies is the use of innovative technologies. Most recognize that we have significant scientific and technological capabilities if those capacities can be focused on these challenges. In particular, the innovative technologies in specialty crop production used to confront these challenges are often those associated with applications based on sensors, spatial positioning, and site-specific management. One function of the USDA Specialty Crop Research Initiative (SCRI) is to encourage the development of innovative technologies through collaborative processes involving both private sector research facilities and land grant universities. A critical dimension in this process is to understand the capacity of the target audience to adopt the resulting technological innovations. These technologies may require local support, extensive training, or involvement of a third party vendor, or significant financial investment. This capacity and support can vary by industry, geography or both. Previous research has demonstrated that without adequate understanding of these constraints, the adoption process can be delayed or even stopped. This outcome, as unfortunate as it may sound, is a legitimate decision since adoption without the necessary and sufficient support would result in further harm to the industry. Consequently, understanding the capacity to adopt innovative scientific applications by a specific target audience is a critical dimension of any research initiative. This USDA Agriculture and Food Research Initiative project (Precision Canopy and Water Management of Specialty Crops through Sensor-Based Decision-Making) under the Specialty Crop Research Initiative is focused on developing a decision support system to implement canopy management and irrigation management based on sensor data for fruit and nut crops. The specific objectives of this project were to (1) measure canopy architecture and PAR absorption, (2) detect soil and plant water status, (3) develop a Universal Navigation Computer (UNC), (4) develop a visualization and decision support system, (5) develop a variable rate water application system, (6) conduct economic analysis, and (7) evaluate social implications of these technologies. The project was initiated in 2010 under the leadership of Dr. S.K. Upadhyaya from the University of California at Davis. An original survey instrument to measure capacity to adopt was developed based on a stakeholder meeting held in Phoenix, AZ in 2011 and from comments and suggestions from project Co-PIs. The early drafts of the survey were distributed starting in August 2011. Following a lengthy and comprehensive set of revisions, a final version of the survey was released to the project team in February 2012 where the focus was on canopy management and irrigation management. The 2011 survey was made available to the project team in multiple formats including a web-based method using QuestionPro, a commercial survey site under

contract by AgInfomatics. There were also pdf and MS Word versions that could be emailed to growers or printed and distributed individually or at meetings. The project team was provided the address and log in information for the online survey and instructions on how to bring this to the attention of growers of the specialty crops associated with this project. It was suggested that these log on instructions could be communicated with e-mail, embedded with regular newsletters, or integrated into presentations at Extension or other outreach events. For the print version of the survey, the investigators were asked to provide them to growers who could benefit from the project objectives. Simple instructions were included to try and avoid introducing any bias into the sample process. Some surveys were passed out at meetings, attached to e-mails, and others were sent through the mail. The amount of participation in the distribution techniques varied significantly between the states involved in the project. For the 2011 survey, only one grower used the online survey system. The print version of the survey was more successful when it was distributed and retrieved at grower meetings. The end result was three different data sets, one consisting of 75 completed surveys from wine grape growers in California, a second one consisting of 11 completed surveys from nut and grape growers in California, and a third set consisting of 21 completed surveys from nut growers primarily from New Mexico, Arizona, Texas and Mexico. In addition, two surveys from Washington State were received, but were designed around an earlier version of the survey. The same was the case for eight surveys from Arizona, and were not compatible with the 2011 survey. The 2011 Respondents The first section of this report presents frequencies of survey results from 74 growers from California and 1 from Arizona that grew crops in California. All respondents grew grapes for wine as their primary crop. In addition there was one producer growing table and juice grapes, one growing apples and table grapes, and one growing almonds and walnuts in addition to wine grapes. Approximately 89% of the respondents grew some or all of their crops in the Napa Valley, with the remaining growing crops in 23 other counties. There were 13 respondents growing crops in multiple California counties. All these responses will be labeled ‘California Grapes” in the tables for the 2011 results. These results also present frequencies from 21 respondents, 13 from New Mexico, 3 from Arizona, 1 from Texas, 2 from Mexico and 2 from undisclosed locations. The majority (95%) of respondents grew pecans as their primary crop, with one growing apples exclusively. In addition to pecans, one grower grew apples and one grew wine grapes, cherries, and pistachios. These responses will be labeled “AZ & NM Pecans” in the tables of the 2011 results for convenience despite this diversity in crops produced. The 2011 results also presents frequencies from 11 respondents from CA, 8 of who grow nuts, either walnuts (7), almonds (2), and pecans (1), and 7 of who grow wine grapes of which only two are exclusively wine grapes, and one grape juice grower. Four growers listed their primary crop as walnuts, 5 as wine grapes, 1 as almonds, and 1 as juice grapes. These responses will be labeled “CA

Walnuts/Grapes” in the tables of the 2011 results. 2011 Results Canopy management practices of the growers are summarized in Table 1. Significant differences emerge between canopy management in grapes versus those with nuts. Some nut growers report no pruning, and of those who do, manual pruning is the preferred method. Most of the canopy management techniques, other than pruning, used in the grape industry were not being employed by the nut growers. The majority of grape growers reported using an array of practices including manual pruning, mechanical pruning, hand or mechanical leafing, shoot positioning, lateral removal, and shoot tying. Table 1. Canopy management practices currently employed.

Management Practice CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % No Pruning/Hedging 0.0 9.5 9.1 Manual pruning 97.3 76.2 100.0 Mechanical pruning/hedging

36.5 61.9 9.1

Hand or mechanicalleafing

90.5 0.0 9.1

Shoot positioning 97.3 4.8 18.2 Lateral removal 94.6 19.0 9.1 Shoot tying 64.9 4.8 18.2 Growth regulators 4.1 9.5 18.2

Growers were asked what data they currently collect relative to their most important specialty crop. This data would be related to plant and soil water status, and environmental conditions to use water efficiently while enhancing crop yield and/or quality (Table 2). Knowing the type of data currently being collected is important as many of the techniques being promoted as part of this project are very data dependent. Most California grape growers (91.9%) use weather data, visual observations (90.5%), and close to three quarters monitor crop yield (78.4%) and crop quality (73.0%). About two thirds (63.5%) monitor soil moisture, plant water potential (71.6%), crop phenology (64.9%), and consult historical data (67.6%). About half (55.4%) use aerial imagery. A few growers, < 20%, use sap flow sensors and monitor soil temperature. Overall, the wine grape growers in California collect and use a considerable amount of data in their cropping operation. A small number of growers use other sensors that measure leaf water potential, soil resistance, vapor pressure and vine dimensions (dendrometers). Neither the pecan growers from Arizona and New Mexico nor the walnut and grape growers from California collected as much information as this first group. Two thirds (66.7%) of the AZ and NM nut growers used visual information, and

just over half (52.4%) used weather information. Soil moisture information was used by just under a half (47.6%), and approximately two fifths used crop yield, crop quality, and historical data. Just under three quarters of the California walnut and grape growers used weather and soil moisture data. About two thirds (63.6%) used visual observations, and only historical data (36.4%) had at least a third of the growers using that data type. Table 2. Data currently collected in growers most important specialty crop.

Data Collected CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Weather 91.9 52.4 72.7 Soil Moisture 63.5 47.6 72.7 Soil Temperature 17.6 9.5 0.0 Plant Water Potential 71.6 4.8 0.0 Crop phenology (e.g., fruit size)

64.9 9.5 18.2

Crop yield 78.4 38.1 27.3 Crop quality 73.0 38.1 27.3 Aerial imagery 55.4 9.5 9.1 Sap flow sensors 16.2 0.0 0.0 Historical data 67.6 42.9 36.4 Visual observations 90.5 66.7 63.6 Other 6.8 9.5 18.2 No Response 0.0 0.0 1.0

There are diverse sets of software packages that may be used for data management, visualization, interpretation, and analysis of data from the orchards (Table 3). The majority (85.1%) of California grape growers used spreadsheet software, but the use of other software packages fell off significantly after this one. About one-third (33.8%) of these growers used GIS, and of those, about 60% used a mapping software. About 28.4 % used mapping software alone. Around a quarter (27%) of the growers used software associated with a contract or consulting service that likely included some sort of GIS or mapping software. Only 16.2% of the growers reported using accounting software, and again this is probably associated with consulting services. About 20% of these growers used other software, primarily custom software packages for wine growers. The pecan growers used minimal software packages with most (52.4%) being associated with the use of spreadsheets. A third used accounting software, but there was little reported use of the other software options. The California walnut and grape growers had a minority using spreadsheet packages (36.4%), accounting (27.3%), and software associated with contract or consulting services (18.2%).

Table 3. Software packages currently used for data management, visualization, interpretation, and analysis. Software Packages CA

GrapesAZ&NM Pecans

CA Walnut/Grape

% % % Spreadsheet 85.1 52.4 36.4 Accounting 16.2 33.3 27.3 Geographic Information System

33.8 14.3 9.1

Mapping software 28.4 4.8 9.1 Software associated with Contract or Consulting Services

27.0 4.8 18.2

Other 20.3 4.8 27.3 No Response 0.0 0.0 0.0 The next question (Table 4) attempted to learn about how managed inputs are applied. The overwhelming majority (91.9%) of California grape growers apply managed inputs on a site-specific basis. Just under a third (32.4%) said they determine needs and then apply to all of the crops. Only 14.9% said they apply some of their nutrients on a schedule basis during the year. This compares to a little over half (52.4%) of the pecan growers in AZ and NM who apply managed inputs on a scheduled basis. A third determine needs, and then applies them to all crops, and just under a fifth (19.0%) manage these inputs using site-specific techniques. A similar pattern is found with the CA walnut and grape growers where 45.5% reported managing inputs on a schedule basis, and just over a third (36.4%) saying they determine need and then apply, or manage inputs using site specific techniques. Table 4. Method of applying managed inputs in growers’ primary specialty crop. Method of Applying Managed Inputs

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % On a schedule basis during the year

14.9 52.4 45.5

Determine need and then apply to all crops

32.4 33.3 36.4

Variably applied depending on site-specific needs

91.9 19.0 36.4

No Response 0.0 0.0 0.0

A significant portion of the research objectives are linked to the use of sensors, and therefore we asked the growers about their level of comfort with sensor-based data (Table 5). About one fifth (21.6%) of the CA grape growers did not use it. Another 9.5% said it was difficult to manage or use. About two fifths (41.9%) said

they were comfortable if the software programs did a good job managing data and interpreting it in graphical formats. Just over a quarter (27.0%) said they were very comfortable with sensor-based data on their farms. With respect to the level of comfort with sensor-based data with AZ and NM pecan producers, two thirds did not use it. Only about one in five (19%) said that they were very comfortable as it plays a critical role in their operation. One found it difficult to use and about 10% said it had value if their software programs did a good job managing their data and interpreting it in graphical formats. A more mixed pattern emerges with the CA walnut and grape growers. About one third (36.4%) did not use it, and one grower found it difficult to manage. Just over a quarter (27.3%) said were very comfortable as it plays a critical role in their operation. Another 27% reported that it had value if their software programs did a good job managing their data and interpreting it in graphical formats. Table 5. Level of comfort with sensor-based data on growers’ farms.

Level of comfort CA Grapes

AZ&NM Pecans

CA Walnut/Grape

% % % Don’t use it 21.6 66.7 36.4 Find it difficult to manage and use 9.5 4.8 9.1 Has value if there are software programs that manage and interpret data in graphic formats

41.9 9.5 27.3

Very comfortable as it plays a critical role in my operation

27.0 19.0 27.3

No Response 0.0 0.0 0.0 The use of maps as a medium to view data and provide interpretations for grower fields also had mixed use (Table 6). Among CA grape growers there were just under a third (31.1%) who said they either never use them or do not use them much. On the opposite end of the spectrum are the 29.7% of growers who stated these maps are an integral part of their management program. The largest response category (39.2%) came from those who said they use maps for specific types of management decisions. In AZ and NM the use of maps as a medium to view data and provide interpretations for grower fields had minimal use. Just over four fifths (80.9%) of the pecan growers reported they do not use them much or not at all. Only 14% of these growers use maps for specific management decisions, but not general use. One respondent (4.8%) reported the use of maps as an integral part of management. A similar pattern is found among the CA walnut and grape growers where the use of maps as a medium to view data and provide interpretations for fields or orchards was minimal. Four fifths (82%) said they do not use them much or not at all. Only 1 respondent uses maps for specific management decisions, but not general use. Again, there was 1 respondent who uses maps as an integral part of management.

Table 6. Use of maps by growers as a medium to view data on their farms.

Use of maps CA Grapes

AZ&NM Pecans

CA Walnut/Grape

% % % Never use them 14.9 57.1 36.4 They can be interesting, but I don’t use maps very much

16.2 23.8 45.5

I use them only for specific types of management decisions

39.2 14.3 9.1

Data in the form of maps are an integral part of my management

29.7 4.8 9.1

No Response 0.0 0.0 0.0

Canopy management (Table 7) is critical to grape quality for all of the respondents with >90% stating it is essential and the remaining growers stating it is very important. Just over half (56.8%) of these CA grape growers said canopy management was essential for crop yield, and another third (32.4%) said it was very important. Only one in ten (10.9%) said canopy management was somewhat or not important relative to crop yield. Table 7. The relative importance of canopy management to crop quality and crop yield.

How critical is canopy management to crop quality

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Essential 90.5 33.3 36.4 Very Important 9.5 38.1 45.5 Somewhat Important 0.0 19.0 18.2 Not a Concern 0.0 4.8 0.0 No Response 0.0 0.0 0.0

How critical is canopy management to crop yield

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Essential 56.8 28.6 45.5 Very Important 32.4 42.9 36.4 Somewhat Important 9.5 19.0 18.2 Not a Concern 1.4 4.8 0.0 No Response 0.0 0.0 0.0

There was a similar pattern among the pecan growers. Canopy management was considered to be essential or very important by about seven of every ten respondents for both crop yield (71.5%) and crop quality (71.4%). The remaining 23.8% said canopy management is either somewhat important or not a concern. The CA walnut and grape growers also strongly supported the importance of canopy management. Canopy management is essential or important to about 82% of respondents relative to crop yield and quality and somewhat important to the remaining 18%. None thought it was not a concern. The respondents gave slightly more importance of canopy management for crop yield than crop quality. It was noted that large majorities of growers acknowledged the importance of canopy management for their operation. A logical follow-up question therefore, is the status on canopy management on their farm or orchard (Table 8). The majority of CA grape growers (79.7%) felt their canopy management needed some tweaking and regular attention. Less than a fifth (17.6%) said their current level of canopy management was optimal. Two growers reported their current canopy conditions limited crop yield and/or quality. The majority AZ and NM pecan growers (66.7%) felt their canopy management needed tweaking or regular attention. About 14% felt their current level of canopy management was optimum while one thought their current canopy conditions limit crop yield and/or quality. It is important to note that about 14% of these respondents were not sure of the status of their canopy management. About half the CA walnut and grape growers (55%) felt their canopy management needed tweaking or regular attention. Only 1 felt their current level of canopy management was optimum while 18% thought their current canopy conditions limit crop yield and/or quality. Again we have some (18%) who were not sure about the status of their canopy management efforts. Table 8. Current level of canopy management on grower’s primary specialty crop.

Current level of canopy management

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Optimum, no improvements needed

17.6 14.3 9.1

Needs tweaking and regular attention

79.7 66.7 54.5

Canopy condition limits crop yield and/or quality

2.7 4.8 18.2

Not sure 0.0 14.3 18.2 No Response 0.0 0.0 0.0

Another map that could be used by growers would illustrate crop yield, canopy density, and canopy distribution (Table 9). More than half (59.5%) of the CA grape growers thought a map of crop yield or canopy density and distribution would be essential or very important for both crop load management (58.1%) and yield (59.5%). Just over one third thought it was somewhat important for yield

(35.1%) or crop load management (37.8%). Few thought it was not a concern. More than 70% of pecan growers thought that a map of crop yield or canopy density and distribution would be essential or very important for crop load management. Another two thirds (66.7%) said the same regarding crop yield. Just over a quarter (28.5%) said a map would only be somewhat or not important for crop load management, and a third (33.3%) said this about crop yield. Almost three quarters (72.7%) of CA walnut growers thought a map of crop yield or canopy density and distribution would be essential or very important for crop load management. Just under half (45.5%) felt the same about crop yield. Overall, crop yield was perceived as being less important than crop load management for CA walnut growers. Table 9. The relative importance of a map of crop yield or canopy density and distribution for crop load management and crop yield.

Importance of a map of crop yield or canopy density and distribution relative to crop load management

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Essential 9.5 9.5 9.1 Very Important 50.0 61.9 63.6 Somewhat Important

35.1 19.0 18.2

Not a Concern 5.4 9.5 9.1 No Response 0.0 0.0 0.0

Importance of a map of crop yield or canopy density and distribution relative to crop yield

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Essential 9.5 23.8 0.0 Very Important 48.6 42.9 45.5 Somewhat Important 37.8 23.8 36.4 Not a Concern 4.1 9.5 9.1 No Response 0.0 0.0 0.0

An expected response among growers when asked about the potential adoption of new technology is the economic implications. This issue is explored in Table 10 when we asked growers about the potential return on investment in regard to management practices related to this project. Just over two-fifths of (43.2%) of the grape growers said that crop load management would have the greatest potential for return on investment (ROI) from investment in equipment or services that

provide high quality data on crop canopy. Investment in predicting crop yield was viewed by 37.8% of these growers as having the next greatest potential return on investment. Food quality and pruning had less than one in ten selecting them. Over half (57.1%) of the pecan growers selected pruning as having the greatest potential for return on investment (ROI). Yield prediction was selected by 28.6% of these growers as providing the greatest potential for influencing ROI while crop load management was selected by 14.3% of these nut growers. The CA walnut and grape growers had over half (54.5%) identifying pruning as having the greatest potential or return on investment. Yield prediction was selected by a little over a quarter (27.3%) while 18.2% said crop load management was most important for ROI. No one selected food quality issues as having a potential impact for ROI. Table 10. Management practice with the greatest potential for return on investment (ROI).

Greatest potential for ROI

CA Grapes

AZ&NM Pecans

CA Walnut/

% % % Pruning 9.5 57.1 54.5 Crop load management 43.2 14.3 18.2 Yield prediction 37.8 28.6 27.3 Food quality issues 4.1 4.8 0.0 Other 5.4 4.8 0.0 No Response 0.0 0.0 0.0

The next section of the survey moved from canopy management to practices related to irrigation and is illustrated in Table 11. California wine grape growers use a number irrigation water management technology. Irrigation scheduling is used by over four fifths (85.1%) of the growers. More than half (62.2%) of the growers used soil moisture and plant water status sensors (56.8%). A few growers used other sensors with about 15% using automated irrigation control technologies. Most pecan growers (85.7%) use irrigation scheduling and one third use soil moisture sensors. A few use plant water status sensors (19%). Two respondents (9.5%) use automated irrigation control technologies. Almost three quarters (72.7%) of the CA walnut and grape growers use irrigation scheduling, and 54.5% use soil moisture sensors. None use plant water status sensors and only 1 respondent uses automated irrigation control technologies. One respondent said they were using soil temperature sensors with soil moisture sensors. The combination of increased competition for water by non-agricultural interests combined with the uncertainty of climate change increases the salience of science and technology related to water management. Interest in this science and technology, however, is strongly influenced by the belief of growers as to whether they will face a significant irrigation water shortage within the next ten years. The best estimate of the probability that CA grape growers will face a significant water shortage for irrigation within the next ten years ranged from 0% (no chance) to 100% (certain this will happen). About 20% of these growers felt this was certain (100% probability) and 38% placed the probability at 75% or higher. The mean

response was 56%. Pecan growers’ best estimate of the probability of a significant water shortage for irrigation within the next ten years ranged from 50% to 100%. About 45% of these growers felt this was certain (100% probability) and 70% placed the probability at 75% or higher. The mean response was 85%. The CA walnut and grape growers’ best estimate of the probability they will face a significant water shortage for irrigation within the next ten years ranged from 0% to 100%. About 18% of the growers felt this was certain (100% probability) and 46% placed the probability at 75% or higher. The mean response was 58%. Table 11. Use of irrigation water management technologies.

Irrigation water management technologies used

CA Grapes

AZ&NM Pecans

CA Walnut/Grape

% % % Irrigation scheduling 85.1 85.7 72.7 Soil moisture sensors 62.2 33.3 54.5 Plant water status sensors

56.8 19.0 0.0

Automated irrigation control technologies

14.9 9.5 9.1

Other 9.5 9.5 9.1 No Response 0.0 0.0 0.0

Both the increasing sophistication of the technology for managing irrigation water combined with the increasing complexity of the market and regulatory setting increases the probability that consultants will be employed by these specialty crop growers. As seen in Table 12, 15% of the CA grape growers always used consultants to assist them in water management on their farm. Just over a half (52.7%) said they use consultants for water management sometimes. About one third (32.4%) of these growers never used consultants to assist in water management on their farms. A similar number (14.3%) of pecan growers always used consultants to assist them in water management. Another 14.3% reported using consultants sometimes. The largest group, about two thirds, said they never used consultants to assist in water management on their farms. Table 12. Use of consultants to assist growers in the management of water on their farm.

Use consultants in water management

CA Grapes AZ&NM Pecans

CA Walnut/Grape

% % % Always 14.9 14.3 9.1 Sometimes 52.7 14.3 36.4 Never 32.4 66.7 54.5

Consultants were always used on 9.1% of the CA walnut and grape farms to

manage water. A little over a third (36.4%) reported using these professional resources some times, but the largest category was the 54.5% who said consultants were never used in the management of water on their farms. Various types of sensors have been identified as having significant potential for managing variability in conditions that influence crop quality and quantity. Growers were asked how important the use of plant or soil moisture sensors would be in their ability to influence specific aspects of their operation. They were asked to consider the impact of these sensors relative to four key dimensions of crop production identified earlier. These are the potential for reducing labor, the potential for reducing water use, potential for increasing yield, and potential for increasing crop quality. The results are presented in Table 13. Table 13. Importance of plant or soil moisture sensors to influence specific aspects of a grower’s operation.

Importance of sensors relative to reducing labor

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Essential 4.1 4.8 9.1 Very Important 14.9 19 18.2 Somewhat Important

32.4 19 27.3

Marginal Impact 29.7 14.3 9.1 No Impact 18.9 14.3 27.3 No Response 0.0 28.6 0.0

Importance of sensors for reducing water use

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Essential 21.6 19 9.1 Very Important 44.6 38.1 63.6 Somewhat Important 21.6 0.0 18.2 Marginal Impact 8.1 9.5 9.1 No Impact 4.1 9.5 0.0 No Response 0.0 23.8 0.0

Importance of sensors relative to increasing yield

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Essential 5.4 23.8 36.4 Very Important 32.4 47.6 45.5 Somewhat Important 29.7 4.8 9.1 Marginal Impact 24.3 4.8 0.0 No Impact 8.1 4.8 9.1

No Response 0.0 14.3 0.0

Importance of sensors for increasing crop quality

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Essential 24.3 23.8 27.3 Very Important 51.4 42.9 63.6 Somewhat Important

18.9 9.5 0.0

Marginal Impact 4.1 4.8 9.1 No Impact 1.4 4.8 0.0 No Response 0.0 14.3 0.0

Approximately 62% of the CA grape growers reported (Table 14) they already use soil moisture sensors, and 57% use plant water sensors on their farm. Labor was the least likely category to benefit from these sensors. Reducing water use and increasing crop quality were the most likely to result from the use of these sensors with crop yield increases only important to about one third of the growers. The pecan growers had 37% who reported they already use soil moisture sensors and 19% who use plant water sensors on their farm. Depending on the category, between 14-28% of the respondents did not answer one or all of the category questions. Labor was the least likely category to benefit from these sensors. Reducing water use and increasing crop quality and crop yield were the most likely to result from the use of these sensors. Table 14. Importance of wireless network technologies coupled with real-time water sensors for irrigation management.

Importance of wireless networks with water sensors

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Essential 10.8 9.5 9.1 Very Important 52.7 38.1 36.4 Somewhat Important 23.0 38.1 27.3 Marginal Importance 5.4 4.8 18.2 Not Important 5.4 4.8 9.1 Already use wireless network technologies

2.7 9.5 0.0

No Response 0.0 0.0 0.0 About 25-43% of the respondents rated the value of sensors as marginal, would have no impact, or did not respond to the various categories. There were 54.5% of the CA walnut and grape growers who reported they already use soil moisture sensors on their farm. None reported using plant water sensors. Again, labor was

the least likely category to benefit from these sensors (only 27% essential or very important) while 75% felt they would be essential or very important in reducing water use. Just over four fifths (81.9%) said these sensors would be essential or very important for increasing yield, and 90.9% gave these same ratings relative to the potential to increase crop quality. One person (9%) did not answer this question. Another area where science has made significant advances in recent years is in the development of semi- or fully-automated vehicles (i.e., robots) for use in agriculture. Most growers would be familiar with some of these early or potential applications of this technology. As in the earlier questions about sensors, these specialty crop growers were asked about the potential value of these robots for decreasing labor costs, decreasing water use, increasing yield, and increasing crop quality (Table 15). Table 15. Potential value for semi- or fully-automated vehicles (i.e., robots) in a grower’s farming operations.

Importance of robots for reducing labor costs

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Essential 5.4 9.5 18.2 Very Important 24.3 14.3 27.3 Somewhat Important

24.3 33.3 0.0

Marginal Impact 25.7 9.5 18.2 No Impact 20.3 14.3 27.3 No Response 0.0 19 9.1

Importance of robots for decreasing water use or cost

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Essential 8.1 14.3 0.0 Very Important 36.5 28.6 45.5 Somewhat Important

16.2 14.3 18.2

Marginal Impact 21.6 9.5 18.2 No Impact 17.6 14.3 9.1 No Response 0.0 19.0 9.1

Importance of robots for increasing yield

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Essential 4.1 9.5 0.0 Very Important 17.6 33.3 63.6 Somewhat Important

33.8 23.8 0.0

Marginal Impact 27.0 4.8 9.1 No Impact 17.6 14.3 18.2 No Response 0.0 14.3 9.1

Importance of robots for increasing crop quality

CA Grapes

AZ&NM Pecans

CA Walnut/Grapes

% % % Essential 13.5 9.5 9.1 Very Important 28.4 23.8 54.5 Somewhat Important

21.6 28.6 0.0

Marginal Impact 25.7 4.8 18.2 No Impact 10.8 14.3 9.1 No Response 0.0 19.0 9.1

Most of the CA grape growers said this technology would have little impact relative to their ability to reduce labor costs, an area where many experts see the greatest value occurring. While just under a third (29.7%) said it would be essential or very important for this purpose, they were countered by the 46% who said it would have marginal or no utility for reducing labor costs. The biggest value was relative to decreasing water use or costs as 44.6% said these vehicles would be essential or very important relative to that objective. This was followed closely by the two fifths (41.9%) who said this technology would be essential or very important for increasing crop quality. Pecan growers also did not see much potential for this technology for reducing labor as less than a quarter (23.8%) gave it an essential or very important ranking. The same percent said it would have a marginal or no impact. It is important to point out that a fifth (19%) of the growers did not answer this question. Just over a third of the CA walnut growers gave this technology an essential or very important ranking relative to reducing labor costs. However, this was countered by the 45.5% who said the technology would have marginal or no impact on labor costs. The use of robots for decreasing water use or costs finds a split in grower assessments. Just over two fifths (44.6%) of CA grape growers view robots as essential or very important for this function, but are balanced by the just under two fifths (39.2%) of growers who view it as having marginal or no impact. This same balance is not there for the pecan growers as approximately two fifths (42.9%) of

the pecan growers see robots as essential or very important for reducing water use or costs. There is just under a quarter (23.8%) saying robots will have minimal or no impact relative to water issues. However, again caution is needed as there were 19% of these growers who did not respond to this question. CA walnut and grape growers had 45.5% saying robots would be very important, and 27.3% who said robots would have marginal or no impact. The last two sections of Table 15 relate to the potential or robots to increase yields or crop quality. The CA grape growers had just over a fifth (21.7%) saying robots would be essential or very important for increasing yields, and 41.9% giving this same ranking for robots increasing crop quality. Yet there was also a significant proportion of these CA grape growers who said robots would have marginal or no impact for increasing yields (44.6%) or for increasing crop quality (36.5%). Pecan growers had a similar ranking of the potential for robots in these two areas of crop production, but may have been skewed by the missing data. The CA walnut and grape growers were the most positive relative to the potential for robots to increase yields and crop quality as close to two thirds give robots an essential or very important ranking from yields and crop quality. There is research literature that suggests that farmers often think of a new technology with a sequence of questions. Initially farmers want to know if the technology even exists, and if so, to what extent would it be applicable or fit into their operation. If they see that the technology exists and has potential applicability, then they try to assess if it would be cost effective. Finally, if the innovative technology appears to be cost effective, then the growers begin to assess if they can make it work within their operation. The growers responding to this survey were asked about a production system that could emerge from the objectives associated with this project, and where they are across this sequence of questions. The growers were asked to consider a sensor-based system that is linked to real-time networks that assesses situations, diagnose problems and then manage responses using some form of a decision support system. The results are presented in Table 16. Most CA grape growers felt such a system was feasible as only 8.1% were at the stage of wondering if such a system even exists. Just under a quarter (24.3%) were wondering if such a system could be made applicable to their operation, while the largest category (63.5%) were questioning if such a system could be cost effective. Only 4.1% were wondering if they had the managerial capacity to make such a system work. The pecan growers had a similar array of responses with 9.5% asking if the system existed, 28.6% wondering about compatibility, but only a third assessing the cost effectiveness. Just under a fifth (19.0%) were wondering about their capability to manage such a system. There were 9.5% of the pecan growers who did not answer this question. Almost one in five (18.2%) of the CA walnut and grape growers wondered if such a system even existed. About three fifths (63.6%) were thinking of the costs and benefits of such a system. There were also 9.1% of these growers who did not answer the question.

Table 16. Most important question regarding a production system based on using sensors linked to real-time networks to assess situations, diagnose problems and manage responses for a specialty crop operation.

Current thinking on sensor based systems

CA Grapes

AZ&NM Pecans

CA Walnut/Grape

% % % Does such a system even exist?

8.1 9.5 18.2

Can such a system be made to work in my operation?

24.3 28.6 9.1

Will this system be cost-effective to own and manage?

63.5 33.3 63.6

Can I manage such a system?

4.1 19.0 0.0

No Response 0.0 9.5 9.1 The target audiences for this SCRI research project is geographically diffuse making any sort of assessments very challenging. The project did not have any centralized or universal list of growers targeted by the research, and therefore getting feedback from growers was dependent on the PIs bringing the assessment instrument to the attention of the growers. This procedure had mixed results. This limitation eliminates the potential to generalize any of the findings beyond those who participated in the surveys. Nonetheless, even with this limitation there are some interesting outcomes from this assessment A common pattern found across the questions in this assessment was the diversity in responses. There were no measures of the characteristics of the farm operation, but the apparent willingness to investigate and adopt many of the technologies and techniques associated with the research agenda varies significantly. We are not dealing with a homogeneous audience where there will be universal applicability of the resulting technologies. The challenge is going to be in transforming the basic research into applied research in a manner such that the resulting technologies and techniques apply to the widest possible audience. The diversity in audiences precludes uniformity in Extension and outreach methods. The assessment did not measure where these growers obtain information, advice, and technical recommendations. If this information gathering process is consistent with the large body of previous research in this area, then there will be a mix of public and private sources using multiple channels and media to deliver information ranging from simple to complex. A challenge for the project will be to assess where the limited Extension and outreach resources should be focused for the most efficient use of these resources. Many of the applications being explored by this SCRI research are based on the use of sensors. Yet only a small minority of growers stated that they are

‘comfortable’ with sensors. Widespread applicability of this project will be dependent on increasing the knowledge of the potential role of sensors in modern agriculture. Like the previous observation regarding sensors, it was surprising to find so few using GIS techniques in their orchards. The widespread diffusion of this technology in automobiles and smart phones makes it almost universal in some locations, yet it has found limited application among these growers. As is the case in the automobile and cell phone industry, the key to widespread use was the development of simple yet powerful application packages that were easy to use. This increases the importance and salience of the visualization and decision-support systems associated with this project. Managing water is a major concern for the majority of these growers. The majority of growers also recognize that climate change and increasing demand from non-agricultural users will only increase the salience of this theme. The water conservation and efficiency dimension of the research is a theme that could be used to introduce some of the more complex or unfamiliar aspects of the project. The 2014 Pecan Association Respondents The 2014 survey was organized and implemented at the Western Pecan Growers Association Conference and Tradeshow in Las Cruses, NM on March 3, 2014. Over 200 paper surveys consisting of 13 fixed-format and one open-ended question were passed out to show participants. There were 48 surveys returned at the show with an additional 9 forwarded to AgInfomatics at a later date. 2014 Pecan Association Results Even though the survey was implemented at a Pecan Association meeting, the initial question asked was about the most important crop in the operation. As would be expected, the majority (86.0%) said pecans were their most important crop (see Table 17). There were another 8.8 percent who identified almonds, and 5.3 percent who said walnuts were their most important crop.

Table 17. Most Important Crop in Operation Crop Growers % (n) Apples 0.0 (0) Cherries 0.0 (0) Grape - Juice 0.0 (0) Table Grapes 0.0 (0) Wine Grapes 0.0 (0) Almonds 8.8 (5) Pecans 86.0 (49) Walnuts 5.3 (3)

The next question was one that was used in the other surveys to garner some

measure of the relative size of the operation. Rather than the more complex approach of asking for acreage, gross sales, or perhaps quantity produced, the survey simply asks respondent to judge the size of their operation relative to other operation in the area based on a five point Likert scale. Table 18 illustrates a fairly uniform distribution of operations: 28.1 percent viewed themselves as smaller, 19.3 percent thought they were a little below average, roughly a fifth (22.8%) said they were average, one in ten (10.5%) said they were above average, and 15.8 percent said they were larger than most other growers in their local areas. The survey then turned to the assessment of the precision technologies under consideration. The first question in this genre focused on a precision canopy map that would be used to guide pruning or spacing operations. Respondents were asked about the potential value of such a map (Table 19). Only a few (7.0%) viewed such a map as having no value, and only 8.8 percent said it would have limited value. Just under two-thirds (63.2%) said that it would be valuable, as they need additional information about their operation. The final 12.3 percent said such a map would be essential to generating value for the future of their operation.

Table 18. Size of Operation Relative to Local Area Relative Size Growers % (n) Smaller than most other growers of this crop in this industry in my area.

28.1 (16)

A little below average, but larger than the smaller operations.

19.3 (11)

My operation is close to the average size for this industry in my area.

22.8 (13)

A little above average, but smaller than the large operations.

10.5 (6)

Larger than most other growers of this crop in this industry in my area.

15.8 (9)

Table 19. Importance of Canopy Map for Pruning/Spacing Operations

Value of Canopy Map Growers % (n) Such a sensor, maps and decision tools would have little or no value to the way I manage my operation

7.0 (4)

Such a sensor, maps and decision tools would have limited value in my operation

8.8 (5)

More information about my operation is always needed, and therefore use of this sensor, maps and decision tools would be valuable

63.2 (36)

Such a sensor, maps and decision tools would be essential to generate value for the future of my operation

12.3 (7)

The next question asked about the value of a precision canopy map for nutrient and water management (Table 20). One in ten (10.5%) said they would have no interest in such a map for these functions. Just under a fifth (17.5%) said they would use such a map to guide pruning and the location of new plantings. There were 12.3 percent who said such a map could be used to guide marketing decisions, and a third (33.3%) said such a map would be used to increase efficiency in irrigation management. A few (14.0%) said that this map would be used to focus nutrient management as a way to enhance efficiency. Accelerating the future adoption of canopy maps will be based on a clear understanding of the barriers, perceived or real, growers have regarding this technological package. This was addressed by asking growers about their biggest concern regarding the application of a precision canopy map in their operation (Table 21). There were one in ten (10.5%) who had no concerns, and liked the overall concept. The largest group was the 54.4 percent who said they need more proof that such a system will work for their operation. A very few (5.3%) felt the system would be too complex where too many thing could go wrong. Another 14.0 percent viewed such a technological package as being based on local service providers, and they did not want to take on this dependence. Again, only a very few (5.3%) felt that such a package would not generate sufficient returns relative to the investment required.

Table 20. Importance of Canopy Map for Nutrient/Water Management Role of Services Growers % (n) None, I have no interest in such a service 10.5 (6) I would use the service to guide pruning and new plantings

17.5 (10)

I would use the service to generate predictions of potential yield to guide my marketing decisions

12.3 (7)

I would use the service focus my irrigation management to increase efficiency

33.3 (19)

I would use the service focus my nutrient management to increase efficiency

14.0 (8)

Table 21. Biggest Concern about Canopy Maps Concerns Growers % (n) I like it and have no concerns about such a system

10.5 (6)

I need more proof that the system will work for my operation

54.4 (32)

The system sounds too complex where too many things could go wrong

5.3 (3)

I may have to depend on a local service provider for activities I would rather do myself

14.0 (8)

I think the service would cost too much relative to the value I would receive

5.3 (3)

A critical question for allocating scarce educational resources in the future would be the willingness of growers to learn about precision canopy maps. This was addressed with a direct question on whether they would be willing to learn about sensor-developed canopy maps. The majority (61.4%) said “yes” they would be willing to learn about this technology. There were 10.5 percent who were not willing, and roughly one-fifth (19.3%) who said they were not sure. While these results signal that such an educational program could be developed, more attention needs to be given to finding out why that fifth was not sure they wanted to learn about this technology. The survey next moved from the canopy map to irrigation management. The first question in this area begins by trying to understand what practices growers use to guide irrigation (Table 23). Respondents were told they could check more than one practice. Two-thirds (66.7%) schedule irrigation based on weather data. Another two-fifths (40.4%) use soil moisture sensors. Only a few said they used more sophisticated methods associated with plant water sensors (8.8%), automated irrigation control techniques (8.8%), or the use of a computer program to guide water application (3.5%).

Table 22. Willingness to Learn About Sensor-Developed Maps Willingness Growers % (n) Yes, I would be willing to learn more 61.4

(35) No, not at this time 10.5 (6) I am not sure 19.3

(11)

Table 23. Current Use of Irrigation Practices Name of Current Practices Growers % (n) Irrigation scheduling based on weather data 66.7

(38) Soil moisture sensors 40.4

(23) Plant water status sensors 8.8 (5) Automated irrigation control technologies 8.8 (5) A computer program that guides the application of water in my operation

3.5 (2)

Growers recognize that there are certain times during the crop production cycle (e.g., nut filling stage for pecans) when getting the right amount of water on the tree is critical. The next question attempts to get at whether growers use different water management strategies at different times during the production cycle (Table 24). Roughly a fifth (19.3 %) said they use the same techniques all year long. The same number (19.3%) said it depends on the specific weather patterns each year on

whether they change. Just under a third (31.6%) said they collect more information during critical crop periods in order to guide water management decisions. Another 19.3 percent said that the availability and/or price of water during critical periods determined whether they changed water management practices.

Table 24. Change in Water Management across Crop Cycle Changes, if any Growers % (n) No, I use the same techniques to guide water decisions all year long

19.3 (11)

Yes, I collect more information during critical periods

31.6 (18)

I am not sure as it really depends on the weather for each year

19.3 (11)

I change the frequency of my irrigation during crop peak demand period depending on the availability/price of water

19.3 (11)

Another precision technology package is one based on a wireless sensor network that monitors water status and feeds this information to a computer where software guides irrigation decisions. Growers were asked if such a wireless sensor system would have value in their operation (Table 25). Only one in ten (10.5%) said such a system would have no value, and only 15.8 percent said it would only have limited value. Just over half (57.9%) said such a system would provide information in a situation where more information is always valuable. A few (5.3%) said that such a system would be essential to generating value in their future operation.

Table 25. Use of Wireless Sensor Network for Irrigation Potential for Water Sensor System Growers % (n) Such a network of sensors and computer software would have little or no value to the way I manage my operation

10.5 (6)

Such a network of sensors and computer software would have limited value in my operation

15.8 (9)

More information about my operation is always needed, and therefore this network of sensors and computer software would be valuable

57.9 (33)

Such a network of sensors and computer software would be essential to generate value for the future of my operation

5.3 (3)

As was the case for a precision canopy map, understanding concerns about a water sensor system is important to guide future research and outreach efforts (Table 26). A few (8.8%) said they like such a system and have no concerns. Very similar to the canopy map, a majority (56.1%) want more proof that such a system will work in their type of operation. Then there are a few (7.0% who think it is too complex to be feasible, and the 10.5 percent who wonder if the return on investment would be sufficient.

Table 26. Main Concerns About Variable Rate Irrigation Management Concerns Growers % (n) I like it and have no concerns about such a system

8.8 (5)

I need more proof that this type of irrigation system will work for my operation

56.1 (32)

This irrigation system sounds too complex where too many things could go wrong

7.0 (4)

I may have to depend on a local service provider to make the system work

0.0 (0)

I think the system would cost too much relative to the value I would receive

10.5 (6)

As was the case in the earlier survey, respondents to this survey were asked to specify a probability that their growing area would face a “significant” water shortage in the near future. The mean probability was 59.5% with a median value of 70. The mode was a 100% where approximately a fifth of the respondents answered this way. The next series of questions focused on the importance of the benefits of new technology. Growers were asked about the importance of often-cited benefits of new technology, and asked about the importance relative to their operation. The first question asked about new technology that can reduce labor costs (Table 27). While an approximate quarter (24.6%) said it was essential that new technology reduce labor costs, and almost equal number said it was only very important (26.3%) or somewhat important (24.6%). Only a few said this feature of new technology was of marginal importance (5.3%) or not important (7.0%).

Table 27. Importance of Reducing Labor Costs for New Technology Importance Growers % (n) Essential 24.6

(14) Very Important 26.3

(15) Somewhat Important 24.6

(14) Marginal Importance 5.3 (3) Not Important 7.0 (4)

The next attribute for new technology was the importance of either reducing water use or the cost of water use (Table 28). Here there is more agreement on the importance of this attribute as two-fifths (40.4%) said reducing use or costs was essential for a new technology. Just over a quarter (28.1%) said it was very important, and only 12.3 percent said it was only somewhat important. A few (5.3%) said this attribute was of marginal importance, and none said it was not important.

Table 28. Importance of Reducing Water Use/Cost for New Technology

Importance Growers % (n) Essential 40.4

(23) Very Important 28.1

(16) Somewhat Important 12.3 (7) Marginal Importance 5.3 (3) Not Important 0.0 (0)

The next attribute was the ability of new technology to increase crop yield (Table 29). Half (50.9%) said such an attribute was essential, and another 29.8 percent said it was very important. There were 5.3 percent who said increasing crop yield was only somewhat important, and 8.3 percent said it was of marginal importance.

Table 29. Importance of Increasing Crop Yield for New Technology

Importance of New Technology – Increase Yield

Growers

% (n) Essential 50.9

(29) Very Important 29.8

(17) Somewhat Important 5.3 (3) Marginal Importance 8.3 (8) Not Important 0.0 (0)

The last attribute focused on the ability of a new technology to increase the quality of the crop being produced (Table 30). Very similar to the responses for yield, there were approximately half (47.4%) who said improving crop quality was essential for any new technology, and 31.6 percent said this was very important. There were 7.0 percent who said this attribute was somewhat important, and only a few said it was of marginal or no significance.

2014 Oregon Hazelnut Meeting On August 6th, 2014 a meeting was held in Oregon regarding hazelnut production issues. Approximately 150 participants were in the audience, and responses to the clicker survey ranged from the low 140s to the mid-150s. The survey only focused on canopy management, as that was the objective of Oregon’s involvement in the overall project.

Table 30. Importance of Increasing Crop Quality for New Technology

Importance of New Technology – Increase Quality

Growers

% (n) Essential 47.4

(27) Very Important 31.6

(18) Somewhat Important 7.0 (4) Marginal Importance 1.8 (1) Not Important 1.8 (1)

2014 Oregon Hazelnut Results Meeting participants were asked to identify the one crop that was most important to their operation (Table 31). The overwhelming majority identified hazelnuts (95.5%) as their most important crop. A few (1.9%) said it was grapes for wine, apples (1.3%), and there was one grower who said the most important crop was walnuts.

Table 31. Most Important Crop in Operation

Crop Growers % (n) Apple 1.3 (2) Cherries 0.6 (1) Grapes for Juice 0.0 (0) Table Grapes 0.0 (0) Grapes for Wine 1.9 (3) Almonds 0.0 (0) Hazelnuts 95.5 (149) Walnuts 0.6 (1)

The next question turned to the relative size of their operation. Growers were asked to classify the size of their operation relative to other growers in their area (Table 32). Just over a quarter (27.5%) said they were smaller than most other growers while 17.4 percent said there were just a little below average. Approximately a quarter (24.2%) said they were an average size for their production area. A fifth (19.5%) said they were a little above average, and 11.4

percent said they were larger than most other operations in their local production area. The survey then turned to gaining feedback on the technological package associated with precision canopy management. Respondents were asked to identify the importance of this technology for different management functions. The first function that was explored was to use a precision canopy map to guide pruning operations (Table 33). A little over a fifth (22.9%) said such a map would be very valuable for this function, and another 36.2 percent said it would be somewhat valuable. Fewer than one in ten said it would have limited value (8.6%), or little or no value (4.6%). Pointing out the need for additional research and outreach resources were the 18.5 percent who were not sure or needed more information.

Table 32. Size of Operation Relative to Local Area

Relative Size Growers % (n) Smaller than most other growers of this crop in my area.

27.5 (41)

A little below average, but larger than the smaller operations.

17.4 (26)

My operation is close to the average size for this area.

24.2 (36)

A little above average, but smaller than the large operations.

19.5 (29)

Larger than most other growers of this crop in my area.

11.4 (17)

Table 33. Importance of Canopy Map for Pruning Operations

Importance Growers % (n) Very Valuable 22.9 (36) Somewhat Valuable 36.3 (57) Limited Value 16.6 (26) Little or No Value 5.7 (9) Not Sure – Need More Information 18.5 (29)

The clicker survey then queried the importance of a canopy map for tree and row spacing (Table 34). Approximately two-fifths (40.8%) said such a map would be very valuable for guiding management decisions regarding tree placement or pruning operations. Just over a third (36.2%) said it would be somewhat valuable. Only a few perceived such a map as having limited value (8.6%) or no value (4.6%). A significant number – one in ten – said they were not sure and needed more information.

Table 34. Importance of Canopy Map for Tree/Row Spacing Decisions Importance Growers % (n) Very Valuable 40.8

(62) Somewhat Valuable 36.2

(55) Limited Value 8.6 (13) Little or No Value 4.6 (7) Not Sure – Need More Information 9.9 (15)

The last attribute explored in the survey was relative to using a canopy map to guide nutrient management decisions (Table 35). Participants did not perceive as much value in this technological attribute as they did in the two previous attributes. Only 16.3 percent saw it as very valuable with another 19.0 percent saying a canopy map would be somewhat valuable for guiding nutrient management decisions. Just over a quarter (26.5%) said it would have limited value and almost another quarter (23.8%) said it would have little or no value. However, there is still a significant minority (14.3%) who were not sure and need more information.

Table 35. Importance of Canopy Map for Nutrient Management Importance Growers % (n) Very Valuable 16.3 (24) Somewhat Valuable 19.0 (28) Limited Value 26.5 (39) Little or No Value 23.8 (35) Not Sure 14.3 (21)

The final question in the survey asked participants to identify the main barrier to adopting a precision canopy map in the production of their most important crop (Table 37). A lack of information is the main barrier as the two largest response categories were the 26.8 percent who were uncertain about benefits, and the 24.8 percent who were uncertain about the costs of a canopy map. Others said it would be access to capital (14.1%), labor availability (10.7%), or increase in management time (8.0%). There were 12.1 percent who said there were no barriers to the adoption of such a system.

Table 37. Main Barrier to Adopting Precision Canopy Management Main Barrier Growers % (n) No Barriers 12.1 (18) Labor Availability 10.7 (16) Access to Capital 14.1 (21) Uncertainty about Benefits 26.8 (40) Uncertainty about Costs 24.8 (37) Increase in Management Time 8.0 (12) Learning Curve Too Steep 3.4 (5)

2015 Almond Conference Respondents This survey was conducted at the Almond Conference in Sacramento in January of 2015. An overview presentation was made on the technologies involved, and then the survey was administered using the clicker methodology. This method has been used in other settings to gain insights from growers attending meetings (see Yue et al., 20131). Approximately 120 participants attended, and over a hundred participated in the clicker survey. 2015 Almond Conference Results As was the case for the Pecan Association survey, the first question asked about the most important crop in the operation (Table 38). There were 84 percent who identified almonds with five percent identifying with walnuts and grapes for wine. There were a few growers who said cherries (2%), grapes for juice (1%), table grapes (2%), or pecans (2%) was their most important crop.

Table 38. Most Important Crop in Operation

Crop Growers % (n) Apple 0 (0) Cherries 2 (2) Grapes for Juice 1 (1) Table Grapes 2 (2) Grapes for Wine 5 (6) Almonds 84 (92) Pecans 2 (2) Walnuts 5 (5)

Following the most important crop, the next question asked about the relative size of the operation (Table 39). The respondents were relatively equally divided among the five possible answer categories. There were 19 percent who said they were smaller than most, 20 percent who said they were a little below average, 21 percent who felt they were average, 15 percent who said they were a little above average, and 24 percent who said they were larger than most growers in their local areas. The first production-related question focused on the use of a canopy map to guide pruning operations (Table 40). Growers were asked about the importance of such a map for their operation. A third (33%) said it would be very valuable, and there were 31 percent who said it would be somewhat valuable. There were 16 percent who said it would have limited value, and 13 percent who said it would have little

                                                            1 Yue, C., R. Gallardo, J. Luby, A. Rihn, J. McFerson, V. McCracken, D. Bedford, S. Brown, K. Evans, C. Weebadde, A. Sebolt and A. Iezzoni. 2013. An Investigation of U.S. Apple Producers’ Trait Prioritization—Evidence from Audience Surveys. HortScience 48(11):1378–1384) 

or limited value. There were 8 percent who were not sure about the use of such a map to guide pruning operations.

Table 39. Size of Operation Relative to Local Area Relative Size Growers % (n) Smaller than most other growers of this crop in this industry in my area.

19 (22)

A little below average, but larger than the smaller operations.

20 (23)

My operation is close to the average size for this industry in my area.

21 (24)

A little above average, but smaller than the large operations.

15 (17)

Larger than most other growers of this crop in this industry in my area.

24 (27)

Table 40. Importance of Canopy Map for Pruning Operations

Importance Growers % (n) Very Valuable 33 (37) Somewhat Valuable 31 (34) Limited Value 16 (18) Little or No Value 13 (14) Not Sure 8 (7)

Besides pruning, a sensor-developed canopy map can also be used to guide tree and row spacing decisions (Table 41). There were 50 percent of the growers who said such a map would be very valuable for this function. Another 30 percent said it would be somewhat valuable. There were 9 percent who saw limited value, and 7 percent who said such a map would have little or no value. Four percent were not sure of value. Table 41. Importance of Canopy Map for Tree/Row Spacing Decision

Importance Growers % (n) Very Valuable 50 (56) Somewhat Valuable 30 (33) Limited Value 9 (10) Little or No Value 7 (8) Not Sure 4 (4)

The next question turned to the use of such a canopy map to guide nutrient management (Table 42). Again, half (51%) of the growers said such a map would be very valuable. A quarter (26%) said it would only be somewhat valuable, and 12 percent said it would have limited value. There were a few (3%) who said little or no value, and 10 percent were not sure of the value for this function.

Table 42. Importance of Canopy Map for Nutrient Management Importance Growers % (n) Very Valuable 51 (60) Somewhat Valuable 26 (31) Limited Value 12 (14) Little or No Value 3 (3) Not Sure 10 (8)

The next function of a canopy map turned to water management (Table 43). Growers were asked about the value of a precision canopy map to guide water management decisions. Only two percent were not sure about this capacity, and only three percent said such a map would have limited or little/no value. The majority (65%) said such a map would be very valuable, and another 26 percent said it would be somewhat valuable. Growers see the value of such maps for water management more so than for spacing or nutrient management functions.

Table 43. Importance of Canopy Map for Water Management Importance of Canopy Map for Water Management

Growers

% (n) Very Valuable 65 (75) Somewhat Valuable 26 (30) Limited Value 3 (4) Little or No Value 3 (4) Not Sure 2 (2)

Assessing the barriers to the adoption of a precision canopy map is important to understand (Table 44). Ten percent said they perceived no barriers and are awaiting development of such a system. Relatively few identified labor availability (7%) or too steep of a learning curve as a main barrier. The uncertainty about costs (33%) versus uncertainty about benefits is the main perceived barriers to this technology package. Barriers associated with access to capital or increase in management time only had 10 percent of the growers identifying this barrier.

Table 44. Main Barrier to Adopting Precision Canopy Management Main Barrier Growers % (n) No Barriers 10 (11) Labor Availability 7 (8) Access to Capital 11 (12) Uncertainty about Benefits 24 (27) Uncertainty about Costs 33 (37) Increase in Management Time 10 (11) Learning Curve Too Steep 5 (6)

The survey then turned from canopy management to irrigation management. As was the case with earlier surveys, the first question in this theme began to identify current irrigation practices. Growers were told they could identify up to three different techniques that would be used in irrigation scheduling. The first choice in irrigation techniques is listed in Table 45. Weather or ET data was the most popular technique identified (32%) followed by those who depended on the look or feel of the soil (22%). Close to this were the 19 percent who used soil moisture sensor data to guide irrigation scheduling. Making irrigation scheduling decisions based on the calendar (11%) or the appearance of the plant (11%) rounded out the techniques identified as first choice.

Table 45. First Choice of Irrigation Scheduling Techniques Irrigation Basis Growers % (n) Weather/ET data 32 (23) Calendar Basis 11 (8) Look and feel of soil 22 (16) Soil moisture sensor data 19 (14) Plant appearance 11 (8) Plant water status 4 (3) None of the above 1 (1)

The second choice for current irrigation scheduling techniques is presented in Table 46. The top two-second choices were weather/ET data (27%) and look and feel of the soil (25%). Soil moisture sensor data (17%), plant appearance (14%), and calendar basis (12%) round out the top five-second choices. Table 46. Second Choice of Irrigation Scheduling Techniques

Irrigation Basis Growers % (n) Weather/ET data 27 (28) Calendar Basis 12 (12) Look and feel of soil 25 (25) Soil moisture sensor data 17 (17) Plant appearance 14 (14) Plant water status 5 (5) None of the above 1 (1)

The third choice following on the previous two selections does shift responses somewhat (Table 47). Plant appearance (26%) is the top choice followed by the look and feel of the soil (22%). Calendar basis (17%) is the only other technique that had at least 15 percent identifying it.

Table 47. Third Choice of Irrigation Scheduling Techniques Irrigation Basis Growers % (n) Weather/ET data 14 (14) Calendar Basis 17 (17) Look and feel of soil 22 (22) Soil moisture sensor data 12 (12) Plant appearance 26 (27) Plant water status 10 (10) None of the above 0 (0)

The next series of three questions turn to the importance of various attributes that could be linked to a precision irrigation management system. The first of these attributes is the ability to turn on or off irrigation equipment remotely based on signals from the computer software through a wireless network (Table 48). Just under half (48%) of the respondents said this capacity would be very valuable. There were a fifth (21%) who said this would be somewhat valuable. Fifteen percent said it would have limited value, and 16 percent said it would have little or no value. Table 48. Importance of Ability to Remotely Turn Irrigation System On/Off

Importance Growers % (n) Very Valuable 48 (53) Somewhat Valuable 21 (23) Limited Value 15 (17) Little or No Value 16 (18) Not Sure 0 (0)

The next attribute would be to create management zones based on the information generated by the sensors (Table 49). Just under three-quarter (72%) saw this function as being very valuable, and another 18 percent said it would be somewhat valuable. The remaining ten percent of respondents said there would be limited or no value relative to their operation. Table 49. Importance of Irrigation by Management Zone

Importance Growers % (n) Very Valuable 72 (78) Somewhat Valuable 18 (19) Limited Value 6 (6) Little or No Value 4 (4) Not Sure 1 (1)

The final attribute of this precision irrigation management system was the ability to remotely monitor the plants and/or soil using the wireless sensors (Table 50).

Again, three-quarters (75%) of the respondents said this would be very valuable. Another 18 percent said it would be somewhat valuable. Few saw this attribute as having limited or no value. Table 50. Importance of Remote Monitoring Plant/Soil Sensors

Importance Growers % (n) Very Valuable 75 (80) Somewhat Valuable 18 (19) Limited Value 4 (4) Little or No Value 3 (3) Not Sure 1 (1)

Knowing the potential value of different attributes of such a technological innovation must be balanced by looking at the main barriers to allowing growers to adopt such a system (Table 51). Growers were asked to identify what they thought was the main barrier to using such a system in their operation. The main barrier (48%) was uncertainty over costs of such a system. This was followed by the 22 percent who said there were no barriers. The only other potential barrier to garner at least ten percent was uncertainty about benefits. Table 51. Main Barriers to Adoption of Precision Irrigation Management

Main Barrier Growers % (n) No Barriers 22 (20) Labor Availability 3 (3) Access to Capital 8 (9) Uncertainty about Benefits 10 (11) Uncertainty about Costs 48 (53) Increase in Management Time 5 (6) Learning Curve Too Steep 5 (6)

The last question asked was to ask growers if they would be willing to accept lower yields or costs if the precision irrigation management system could increase water use efficiency (Table 52). Clearly the growers wanted more information on this potential trade-off as the largest response group were the 44 percent who said maybe. There is also a sizable minority who said unlikely (20%) or never (15%) to such a trade-off. Only 11% said definitely, and another ten percent said very likely. Table 52. Willingness to Accept Lower Yield/Cost to Increase Water Efficiency

Willingness Growers % (n) Definitely 11 (12) Very Likely 10 (11) Maybe 44 (48) Unlikely 20 (22) Never 15 (16)

4. Students and Scholars trained:

UC Davis: Three PhD students (Dr. Vasu Udompetaikul, Dr. Rajveer Dhillon, Mr. Francisco Rojo), two MS students (Mr. Kevin Crawford and Mr. Miguel Jimenez-Berni ), and one visiting scholar from Xinjiang Agricultural University, China (Dr. Changie Han) worked on light interception and plant water stress detection aspects. All of these people were associated with Biological and Agricultural Engineering Department at UC Davis. In addition, Plant Sciences Department had visiting scholars from France and Brazil who were exposed to lightbar system technology. Moreover, Aaron Whitlach and Constantint Heitkamp both completed M.S. degrees on this project. Aaron is employed in vineyard management in Napa Valley, and Constantin is pursuing a Ph.D. in Food Science at UC-Davis. Univ. Arizona: Advised Joshua Sherman in his Masters research project, which included linking photosynthetic performance, plant nutritional status, and other physiological variables in New Mexico pecan trees to canopy SPAD (Soil Plant Analysis Development, “greenness”) measurements and mid-day stem water potentials. These data will provide possible baseline information for measuring canopy health from sensor-equipped platforms. Joshua successfully defended his thesis on February 6, 2015 and manuscript will soon be submitted for publication in a peer-reviewed journal.

Washington State University (WSU): Following is the list of students and scholars trained at WSU:

One (1) Post-doctoral Research Associate: Dr. Shao, Yongni Three (3) Ph.D. Students: Osroosh, Yasin; Zeng, Bolong; and Zhang, Jingjin One (1) M.S. Student: Wortman, Riley Two (2) International Exchange Students (Ph.D. Candidates): Li, Lei; and Tong, Junhua Eleven (11) Undergraduate Students: (different involvement, including senior design)

5. Dissemination of results-

UC Davis: Many scientists and engineers from all over the world visited project director’s lab and experimental site over the course of the project. We have also made numerous presentations at local, national and international meetings (Eight different presentations on UC Davis campus to different groups (Monsanto, Seed Central, World Food Center, Microsoft Corporation, Pentair presentation at Robert Mondavi Institute for Wine and Food Science, Viticulture and Enology workshop for Chilean Wine Industry, 2014 Precision Ag Group Meeting, and 2014 CIFAR workshop); Vintage report in Napa, CA; Annual Almond Board Conferences (2011, 2012, 2013, and 2014); Walnut Board Research Conference (2012 and 2014); Annual International American Society of Agricultural Engineers meetings (2011, 2012, 2013 and 2014); International Conference on Precision Agriculture (2012 and 2014); ADAGENG Conference, Turkey (2014); UC Berkeley Business School; Indian Agricultural Research Institute, New Delhi, India; Central Institute of Agricultural Engineering, Bhopal, India; Tokyo University of Agriculture and Technology, Japan; Xinjang Agricultural University, China; University of Agricultural Sciences, Raichur, India; King Saud University, Riyadh, Saudi Arabia; Annually to Tottori University Students from Japan in La Paz, Mexico. In addition, the results of this project have been presented to our Board of Advisers on an annual basis.

Moreover, we have demonstrated the sensor suite/leaf monitor technology at Nickels Field Day (May 15, 2013), during Almond Advisers meeting, Arbuckle CA (May 7th 2014), and to the participants of the 12th International Conference on Precision Agriculture (July 2014). Popular articles about this project have appeared in CA&ES Outlook Magazine (Fall/Winter 2011, p1-5); Western Farm Press (April 20, 2013, p6 and 8), Western Fruit Grower (November/December 2013, p12 and 14). Results of the lightbar work have been presented to growers at 4-6 almond and walnut grower winter meetings each year as well as at 1-2 summer field meetings approximately every other year. In addition, results were presented to hazelnut growers at their annual meeting in Oregon in 2014. Results of our wireless mesh network study were presented on March 4, 2013 at the new fruit and nut tree extension course on campus, organized by the Fruit and Nut Research and Information Center. The course was composed of California growers with experience in a wide range of crops including walnut, almond, pistachio, stone fruit, and pome fruit. The results were also presented at the annual Nickels Field Day in Arbuckle, California, on May 15. This event was attended by growers interested in new technology and research that will enable better crop management.

Results of 2014 study were presented on March 3 and November 17 for the UC Davis Fruit and Nut Tree extension course. The course was composed of California growers with experience in a wide range of crops including walnut, almond, pistachio, stone fruit, and pome fruit. Results were also presented on May 15 at annual Nickels Field Day in Arbuckle, California. The event was attended by extension specialists, consultants, and growers interested in the latest technologies and strategies for improving crop management. A technical presentation was given at the annual meeting of the American Society of Agricultural and Biological Engineers in Montreal on July 14. The audience consisted of primarily students, staff, and faculty researchers from universities worldwide. Another field presentation was given for a tour organized for the International Conference on Precision Agriculture on July 23. The event was attended by growers, consultants, and researchers.

Integration and analysis of the data obtained in the vineyard from four seasons is ongoing. Constantin Heitkamp presented “The Impact of Six Deficit Irrigation Regimes on Yield, Grape, Wine and Sensory Components in Cabernet Sauvignon in 2012 and 2013” April 2, 2014 at UC-Davis Univ Arizona: Two one-hour oral presentations were given at the Western Pecan Production Short Course, Las Cruces, NM, October 20-21, which incorporated major concepts/findings from the pruning direction and pruning frequency studies. The titles of these presentations were: Orchard Design & Planting Pruning and Training Pecan Orchards. An additional 30-minute session presented by Pedro Andrade-Sanchez dealt with options for information-intensive mechanical inputs that fit into the pecan production system in the US Southwest. A 30 minute oral presentation of the final results for the pruning frequency/light interception study is on the agenda for the Western Pecan Growers Association Conference, Las Cruces, March 2, 2015. This conference is the largest gathering of commercial pecan growers in the western US, with typically more than 350 pecan growers from California, Arizona, New Mexico, and Texas in attendance. The PowerPoint

presentation slides for this presentation will be made available on the New Mexico State University Website.

Washington State University: Research outcomes have been presented at both national/international professional conferences, regional industry meetings and field days. The cloud-based decision support software has been provided to UC Davis team for testing. The early feedback is encouraging. The project was also used as a platform for training 10 undergraduate students on their senior design projects. The technology developed for cloud-based decision support software has been published in a top journal (Trans. ASABE) and premiere conference (IEEE International conference on Information Reuse and Integration). The cloud-based software provides a unified platform that can be extended for other decision support applications in agriculture.

6. Changes and Problems

UC Davis: In the original proposal UC Davis was supposed to develop a platform for measuring light interception and develop models to predict potential yield (Objective #1). However, during the course of the project an UAV was added to obtain light interception data. Moreover, in the original proposal we planned to develop a model for potential yield prediction only. We were able to augment the model to explore the possibilities of changing row and tree spacing in an orchard to optimize light capture. This should be very helpful in designing new orchards or during replanting situations. In objective #2, we were supposed to develop a sensor suite to determine plant water stress. We not only developed a successful sensor suite, we also developed a handheld sensor suite of the size of a flashlight that is very easily to carry to the orchard to make measurements. In addition to that, we developed a continuous leaf monitor2, interfaced it to a wireless network and used it to manage precision irrigation. Washington State University: Building decision support systems using cloud computing technology has clear benefits: it provides growers an extensible and scalable decision support platform accessible anywhere via Internet. Nevertheless, this new paradigm-shift approach also requires a continued stream of support, not just to develop but also to maintain software on a cloud-based platform. It should be noted that developing the decision support system on a cloud platform is not part of the proposal of this project. It is an experiment in our effort for building a unified decision support system accessible to growers. Given the benefits of cloud computing, it would likely become a main stream technology for agricultural information systems. We recommend that for future proposals with cloud-based agricultural information systems, a specific portion of budget shall be reserved for maintaining and operating the software on a cloud-based platform.

 

                                                            2 Patent pending.