Pedro Andrade Sanchez

Interests

Research

Drivers of yield variability, site-specific sensor-based management, ground-based high throughput phenotyping

Teaching

n/a

Courses

No activities entered.

Scholarly Contributions

Chapters

• Upadhyaya, S. K., Andrade Sanchez, P., Sakai, K., Chancellor, W. J., & Godwin, R. J. (2009). Chapter 3. Tillage. In Advances in Soil Dynamics Volume III. American Society of Agricultural and Biological Engineers. doi:10.13031/2013.26876
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  ABSTRACT [First paragraph]: Gill and Vanden Berg (1968) defined tillage as a process aimed at creating a desired final soil condition from some initial soil condi-tion through manipulation of soil. For thousands of years humans have been tilling soil using some sort of mechanical devices to create that desired soil condition to in-crease crop production (McKyes, 1985). About 3000 years ago inhabitants of the Eu-phrates and Nile valleys used simple wedge-shaped plows for tilling soil. Iron plows were used in China more than 2000 years ago. In early 1700 A. D. plows similar to the ones used today appeared in parts of Europe. The first tempered cast iron plow was patented in 1785 (by Robert Ransome of Norwich, England). John Deere devel-oped a steel plow with a share and a moldboard in the 1830s. Although some changes in the geometric shape have taken place since the 1930s, the moldboard plow has re-mained virtually unchanged since that time..
• Andrade-Sanchez, P., & Upadhyaya, S. (2007). Using GIS and On-the-Go Soil Strength Sensing Technology for Variable-Depth Tillage Assessment _lit ENGL. In GIS Applications in Agriculture(pp 192-214). Boca Raton FL: CRC Press. doi:10.1201/9781420007718-13
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  In recent years, several soil dynamic research groups have been developing instrumented shanks (i.e., chisels/subsoilers) for the quantification of soil compaction in real-time. The goal of this research is to develop an alternative to standardized cone penetrometer technology commonly used for measuring soil resistance at selected points.

Journals/Publications

• Andrade Sanchez, P., Mitra, S., Roselius, M., McKay, J. K., & Pallickara, S. L. (2023). RADIX+: High-throughput georeferencing and data ingestion over voluminous and fast evolving phenotyping sensor data. Concurrency and Computation: Practice and Experience, 35(8). doi:10.1002/cpe.7484
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  Remote sensing of plant traits and their environment facilitates non-invasive, high-throughput monitoring of the plant's physiological characteristics. However, voluminous observational data generated by such autonomous sensor networks overwhelms scientific users when they have to analyze the data. In order to provide a scalable and effective analysis environment, there is a need for storage and analytics that support high-throughput data ingestion while preserving spatiotemporal and sensor-specific characteristics. Also, the framework should enable modelers and scientists to run their analytics while coping with the fast and continuously evolving nature of the dataset. In this paper, we present Radix+, a high-throughput distributed data storage system for supporting scalable georeferencing, and interactive query-based spatiotemporal analytics with trackable data integrity. We include empirical evaluations performed on a commodity machine cluster with up to 1 TB of data. Our benchmarks demonstrate subsecond latency for the majority of our evaluated queries and improvement in data ingestion rate over systems such as Geomesa
• Sanyal, D., Andrade Sanchez, P., Stackpole, C., & Heun, J. T. (2023). Evaluating an in-situ, low-cost soil CO2 sensor as a soil health assessment tool in agricultural soils. Extension publication AZ2074, 5.
• Sanchez, P. A., López, J. A., Zapata, M. C., Magaña, S. G., Hernandez, D. C., & Lopez, G. F. (2021). Calibración de un prototipo para realizar mediciones continúas de radiación fotosintéticamente activa en sorgo (Calibration of a prototype to perform continuous measurements of photo-synthetically active radiation in sorghum). Revista Mexicana de Ciencias Agrícolas, 171-178. doi:10.29312/remexca.v0i26.2947
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  El presente trabajo tuvo como objetivo calibrar el funcionamiento de un prototipo para medición continua de radiación fotosintéticamente activa (PAR) y validar con pruebas de campo en el cultivo de sorgo (Sorghum spp.) en etapa final de su desarrollo. El prototipo desarrollado se basa en dos sondas de 80 sensores (fotodiodos). Para efectos de calibración se comparó la salida de este prototipo con un sensor comercial AccuPAR modelo LP-80 PAR/LAI Ceptometer (Empresa Decagon), dicha calibración se explica por la pendiente de un modelo de regresión lineal. Los resultados obtenidos fueron una pendiente de 6.22963 μ mol m-2 s-1 (mV x 102)-1 con un coeficiente de determinación de 0.997, cumpliendo con los supuestos estadísticos al 95%. Además, se observan el mapa de la variabilidad en el campo cuyos valores oscilan entre ≤ 794.4 a 2 160 μmol m-2 s-1 indicando el límite inferior que existe mayor follaje y límite superior menor follaje. A partir de los datos generados por el prototipo se observó similitudes con el sensor comercial, lo cual sugiere que dicho prototipo recolecta los datos en menor tiempo y con mayor cobertura de muestreo, aprovechando la ventana de oportunidad del ciclo diurno de radiación solar.
• Perez-ruiz, M., Prior, A., Martinez-guanter, J., Apolo-apolo, O. E., Andrade-sanchez, P., & Egea, G. (2020). Development and evaluation of a self-propelled electric platform for high-throughput field phenotyping in wheat breeding trials. Computers and Electronics in Agriculture, 169, 105237. doi:10.1016/j.compag.2020.105237
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  Abstract The use of high-throughput phenotyping systems in crop research offers a powerful alternative to traditional methods for understanding plant behaviours. These systems provide a rapid, consistent, repeatable, non-destructive and objective sampling method to quantify complex and previously unobtainable traits at relatively fine resolutions. In this study, a field-based high-throughput phenotyping solution for wheat was developed using a sensor suite mounted on a self-propelled electric platform. A 2D LiDAR was used to scan wheat plots from overhead, while an odometry system was used as a local navigation system to determine the precise plot/plant/scan location. Accurate 3D models of the scanned wheat plots were reconstructed based on the recorded LiDAR and odometry data. Seven plots of different wheat cultivars were scanned to calculate the canopy height using LiDAR data, and these results were compared with manual ground truth measurements. Additionally, in each of these seven plots, the NDVI and PRI spectral indices were calculated using low-cost spectral reflectance sensors (SRSs) and an expensive visible/near-infrared (VIS/NIR) spectral analysis system used for reference purposes. The results of the validation showed good agreement between the LiDAR and manual wheat plant height measurements with an R2 of 0.73 and RMSE = 2.63 cm for three days of campaign measurements. A statistically significant linear correlation was observed between the NDVI values obtained with the reference spectrometer and the low-cost SRS; the coefficients of determination were R2 = 0.69 for day 1 and R2 = 0.81 for day 2, suggesting a similar degree of accuracy among both sensing systems. The developed platform and the obtained wheat phenotyping results demonstrated the suitability of the system for acquiring reliable data under field conditions while maintaining a constant low speed and stability during field deployment. The adaptability of the platform to the structure of the crop and the repeatability of data collection throughout the growing season make the system suitable for integration into commercial breeding programmes.
• Andrade Sanchez, P., Heun, J. T., French, A. N., McKay, J. K., Lehner, K. R., Mullen, J. L., Ottman, M. J., & Attalah, S. (2019). Deployment of lidar from a ground platform: Customizing a low-cost, information-rich and user-friendly application for field phenomics research. Sensors, 19(24). doi:10.3390/s19245358
• Andrade Sanchez, P., Thompson, A., Thorp, K., Conley, M., El-Shikha, D., French, A., & Pauli, D. (2019). Comparing nadir and multi-angle view sensor technologies for measuring in-field plant height of upland cotton. Remote Sensing, 11(6). doi:10.3390/rs11060700
• Thompson, A. L., Thorp, K. R., Andrade-sanchez, P., Heun, J. T., Dyer, J. M., White, J. W., & Conley, M. M. (2018). Deploying a Proximal Sensing Cart to Identify Drought-Adaptive Traits in Upland Cotton for High-Throughput Phenotyping.. Frontiers in plant science, 9, 507. doi:10.3389/fpls.2018.00507
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  Field-based high-throughput phenotyping is an emerging approach to quantify difficult, time-sensitive plant traits in relevant growing conditions. Proximal sensing carts represent an alternative platform to more costly high-clearance tractors for phenotyping dynamic traits in the field. A proximal sensing cart and specifically a deployment protocol, were developed to phenotype traits related to drought tolerance in the field. The cart-sensor package included an infrared thermometer, ultrasonic transducer, multi-spectral reflectance sensor, weather station, and RGB cameras. The cart deployment protocol was evaluated on 35 upland cotton (Gossypium hirsutum L.) entries grown in 2017 at Maricopa, AZ, United States. Experimental plots were grown under well-watered and water-limited conditions using a (0,1) alpha lattice design and evaluated in June and July. Total collection time of the 0.87 hectare field averaged 2 h and 27 min and produced 50.7 MB and 45.7 GB of data from the sensors and RGB cameras, respectively. Canopy temperature, crop water stress index (CWSI), canopy height, normalized difference vegetative index (NDVI), and leaf area index (LAI) differed among entries and showed an interaction with the water regime (p < 0.05). Broad-sense heritability (H2) estimates ranged from 0.097 to 0.574 across all phenotypes and collections. Canopy cover estimated from RGB images increased with counts of established plants (r = 0.747, p = 0.033). Based on the cart-derived phenotypes, three entries were found to have improved drought-adaptive traits compared to a local adapted cultivar. These results indicate that the deployment protocol developed for the cart and sensor package can measure multiple traits rapidly and accurately to characterize complex plant traits under drought conditions.
• Zhang, Q., Upadhyaya, S. K., Liao, Q., & Andrade-sanchez, P. (2018). Finite Element Modeling and Simulations to Investigate the Relationship between the Cone Index Profile and Draft Requirements of a Compaction Profile Sensor with Depth. Transactions of the ASABE, 61(1), 37-43. doi:10.13031/trans.12223
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  Previous research conducted using a compaction profile sensor and a standard cone penetrometer over a wide range of soil types and conditions found that the unit pressure acting on the cutting edge, defined as the cone index equivalent (CIE), at a specific depth (d) was related to the cone index (CI) value at that depth, the depth of the cutting edge (d), and the interaction between CI and the depth of the cutting edge (i.e., CI x d) with a very high coefficient of multiple determination irrespective of the soil type and conditions. The objective of this study was to provide an analytical basis for the relationship between CIE and CI. A two-dimensional axisymmetric model for soil-cone interaction and a three-dimensional model for soil-tine interaction were developed using a finite element method (FEM). A non-linear elasto-plastic constitutive behavior along with the Drucker-Prager yield criterion were used to represent the soil cutting process. Simulations studies were conducted in 25 distinct soil types and conditions, and the results indicated a similar relationship between CIE and CI, as observed in the previous research. These results support the existence of a strong theoretical basis for the empirical relationship observed in the previous research.
• Bronson, K. F., Hunsaker, D. J., Mon, J., Andrade-Sanchez, P., White, J. W., Conley, M. M., Thorp, K. R., Bautista, E., & Barnes, E. M. (2017). Improving Nitrogen fertilizer use efficiency in surface- and overhead sprinkler-irrigated cotton in the desert Southwest. Soil Science Society of America Journal, 81(6), 1401-1412.
• Sakai, K., Upadhyaya, S. K., Andrade-Sanchez, P., & Sviridova, N. V. (2017). Chaos emerging in soil failure patterns observed during tillage: Normalized deterministic nonlinear prediction (NDNP) and its application. CHAOS, 27(3).
• Thorp, K. R., Hunsaker, D. J., Bronson, K. F., Andrade Sanchez, P., & Barnes, E. M. (2017). Cotton irrigation scheduling using a crop growth model and FAO-56 methods: Field and simulation studies. Transactions of the ASABE, 60(6), 2023-2039. doi:10.13031/trans.12323
• Pauli, D., Andrade-Sanchez, P., Carmo-Silva, A. E., Gazave, E., French, A. N., Heun, J., Hunsaker, D. J., Lipka, A. E., Setter, T. L., Strand, R. J., Thorp, K. R., Wang, S., White, J. W., & Gore, M. A. (2016). Field-based high-throughput plant phenotyping reveals the temporal patterns of quantitative trait loci associated with stress-responsive traits in cotton. G3-Genes Genomes Genetics, 6(4), 865-879.
• Yang, S., Kaggwa, R. J., Andrade-Sanchez, P., Zarnstorff, M., & Wang, G. (2016). Lint Yield Compensatory Response to Main Stem Node Removal in Upland Cotton (Gossypium hirsutum). Journal of Agronomy and Crop Science, 202(3), 243-253.
• Andrade Sanchez, P., Hunsaker, D., French, A., Waller, P., Bautista, E., Thorp, K., & Bronson, K. (2015). Comparison of traditional and ET-based irrigation scheduling of surface-irrigated cotton in the arid southwestern USA. Agricultural Water Management, 159, 209-224. doi:10.1016/j.agwat.2015.06.016
• Hunsaker, D. J., French, A. N., Waller, P. M., Bautista, E., Thorp, K. R., Bronson, K. F., & Andrade-Sanchez, P. (2015). Comparison of traditional and ET-based irrigation scheduling of surface-irrigated cotton in the arid southwestern USA. Agricultural Water Management, 159, 209-224.
• Thorp, K. R., Gore, M. A., Andrade-Sanchez, P., Carmo-Silva, A. E., Welch, S. M., White, J. W., & French, A. N. (2015). Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics. Computers and Electronics in Agriculture, 118, 225-236.
• Andrade-Sanchez, P., Gore, M. A., Heun, J. T., Thorp, K. R., Carmo-Silva, A. E., French, A. N., Salvucci, M. E., & White, J. W. (2014). Development and evaluation of a field-based high-throughput phenotyping platform. FUNCTIONAL PLANT BIOLOGY, 41(1), 68-79.
• Andrade-Sanchez, P., Gore, M. A., Heun, J. T., Thorp, K. R., Carmo-Silva, A. E., French, A. N., Salvucci, M. E., & White, J. W. (2014). Development and evaluation of a field-based high-throughput phenotyping platform. Functional Plant Biology, 41(1), 68-79.
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  Abstract: Physiological and developmental traits that vary over time are difficult to phenotype under relevant growing conditions. In this light, we developed a novel system for phenotyping dynamic traits in the field. System performance was evaluated on 25 Pima cotton (Gossypium barbadense L.) cultivars grown in 2011 at Maricopa, Arizona. Field-grown plants were irrigated under well watered and water-limited conditions, with measurements taken at different times on 3 days in July and August. The system carried four sets of sensors to measure canopy height, reflectance and temperature simultaneously on four adjacent rows, enabling the collection of phenotypic data at a rate of 0.84ha h-1. Measurements of canopy height, normalised difference vegetation index and temperature all showed large differences among cultivars and expected interactions of cultivars with water regime and time of day. Broad-sense heritabilities (H2)were highest for canopy height (H 2=0.86-0.96), followed by the more environmentally sensitive normalised difference vegetation index (H2=0.28-0.90) and temperature (H2=0.01-0.90) traits. We also found a strong agreement (r 2=0.35-0.82) between values obtained by the system, and values from aerial imagery and manual phenotyping approaches. Taken together, these results confirmed the ability of the phenotyping system to measure multiple traits rapidly and accurately. Journal compilation © CSIRO 2014.
• Andrade Sanchez, P., & Heun, J. T. (2013). Operation of Yield Monitors in Central Arizona: Grains and Cotton. The University of Arizona - Cooperative Extension.
• Andrade Sanchez, P., & Heun, J. T. (2013). Yield Monitoring Technology for Irrigated Cotton and Grains in Arizona: Hardware and Software Selection. The University of Arizona - Cooperative Extension.
• Andrade-Sanchez, P., Kaggwa-Asiimwe, R., & Wang, G. (2013). Plant architecture influences growth and yield response of upland cotton to population density. Field Crops Research, 145, 52-59. doi:10.1016/j.fcr.2013.02.005
• Kaggwa-Asiimwe, R., Andrade-Sanchez, P., & Wang, G. (2013). Plant architecture influences growth and yield response of upland cotton to population density. Field Crops Research, 145, 52-59.
• White, J. W., Thorp, K. R., Salvucci, M. E., Heun, J. T., Gore, M. A., French, A. N., Carmo-silva, A. E., & Andrade-sanchez, P. (2013). Development and evaluation of a field-based high-throughput phenotyping platform.. Functional plant biology : FPB, 41(1), 68-79. doi:10.1071/fp13126
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  Physiological and developmental traits that vary over time are difficult to phenotype under relevant growing conditions. In this light, we developed a novel system for phenotyping dynamic traits in the field. System performance was evaluated on 25 Pima cotton (Gossypium barbadense L.) cultivars grown in 2011 at Maricopa, Arizona. Field-grown plants were irrigated under well watered and water-limited conditions, with measurements taken at different times on 3 days in July and August. The system carried four sets of sensors to measure canopy height, reflectance and temperature simultaneously on four adjacent rows, enabling the collection of phenotypic data at a rate of 0.84ha h-1. Measurements of canopy height, normalised difference vegetation index and temperature all showed large differences among cultivars and expected interactions of cultivars with water regime and time of day. Broad-sense heritabilities (H2)were highest for canopy height (H2=0.86-0.96), followed by the more environmentally sensitive normalised difference vegetation index (H2=0.28-0.90) and temperature (H2=0.01-0.90) traits. We also found a strong agreement (r2=0.35-0.82) between values obtained by the system, and values from aerial imagery and manual phenotyping approaches. Taken together, these results confirmed the ability of the phenotyping system to measure multiple traits rapidly and accurately.
• Andrade Sanchez, P., & Heun, J. T. (2012). From GPS to GNSS: Enhanced Functionality of GPS-Integrated Systems in Agricultural Machines. The University of Arizona - Cooperative Extension.
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  Global Positioning Systems (GPS) are satellite-based navigation systems that utilize a network of earth orbiting satellites. GPS operates well under any weather condition and does not require a subscription fee. GPS is a crucial component of precision agriculture by providing precise location information with very high repeatability. In recent years, GPS have improved in their level of performance and functionality in part because new GPS receivers can track satellites not only from the 32 NAVSTAR satellites of the United States but also from the Russian GLONASS (approximately 24 satellites) systems. These high-accuracy navigation and positioning technologies are categorized as a GNSS (Global Navigation Satellite System). We anticipate that even higher levels of performance will be achieved when the Galileo satellite constellation (European Union) becomes available in 2014 with an initial operating capacity of 18 satellites and expanding to 30 satellites by the year 2020. The changing technology motivates the need for precise definitions. It is clear that GPS will continue to have a remarkable impact on production agriculture. Vehicle guidance or automatic steering control has been the most commonly adopted GPS technology among growers in the last five years. Every year new and improved navigation systems become available with a range of precision capacities to fit most mechanical operations and with new functional capabilities. This publication describes the latest trends in GPS technology and elaborates on topics of extra functionality such as variable rate application, land leveling, and yield monitoring; all are now available from the cab mounted display interface.
• Carmo-Silva, A. E., Gore, M. A., Andrade-Sanchez, P., French, A. N., Hunsaker, D. J., & Salvucci, M. E. (2012). Decreased CO2 availability and inactivation of Rubisco limit photosynthesis in cotton plants under heat and drought stress in the field. Environmental and Experimental Botany, 83, 1-11.
• Hunsaker, D., French, A., Waller, P., Bautista, E., Royer, P., Thorp, K., Andrade-Sanchez, P., & Heun, J. (2012). Irrigation scheduling decision support for field-scale, surface irrigation using remote sensing and ground-based data. Remote Sensing and Hydrology, 352, 414-418.
• Montanha, G. K., Andrade-sanchez, P., Lancas, K. P., Heun, J. T., & Guerra, S. P. (2012). CONSUMO DE COMBUSTÍVEL DE UM TRATOR AGRÍCOLA EM FUNÇÃO DO TIPO DE SOLO E DA PRESSÃO DE INFLAÇÃO NOS PNEUS UTILIZANDO O EQUIPAMENTO CANTEIRADOR. ENERGIA NA AGRICULTURA, 27(2), 44-59. doi:10.17224/energagric.2012v27n2p44-59
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  A mecanizacao agricola impulsionou a agricultura atual melhorando a eficiencia nas operacoes de campo e proporcionando um aumento na produtividade das culturas. Essa intensificacao de atividades mecanizadas na agricultura, contudo, acarretou em maiores gastos energeticos nas propriedades rurais principalmente no consumo de combustivel dos tratores agricolas. O objetivo do trabalho foi comparar o consumo de combustivel de um trator agricola variando-se duas pressoes de inflacao nos pneus para dois tipos diferentes de solo utilizando o equipamento de preparo de solo canteirador para a cultura do algodao irrigado em regioes semiaridas. Os ensaios foram realizados em area pertencente a The University of Arizona com um trator Case 4x2 TDA de 88kW, equipado com sistema de piloto automatico. Os resultados evidenciaram um menor consumo de combustivel utilizando a pressao de inflacao de 124 kPa em solo de textura franco argilo arenosa. Palavras-chave: Ensaio de maquinas, mecanizacao agricola, agricultura de precisao. FUEL CONSUMPTION OF AGRICULTURAL TRACTORS IN COORELATION WITH SOIL TYPES AND TIRE PRESSURES USING IMPLIMENTS SUMMARY: Agricultural mechanization improved the efficiency of field operations by providing an increase in crop production. The intensified mechanization, however, has led to higher energy use mainly in the area of fuel consumption. The objective of this study was to compare the fuel consumption of tractors using two different tire pressures for two different types of soil during tillage with irrigated cotton in semi-arid regions. These tests were performed at the Maricopa Agricultural Center (MAC), an experimental farm belonging to The University of Arizona with a Case 4x2 TDA 88kW equipped with an autopilot system. The results showed lower fuel consumption using a tire pressure of 124 kPa on sandy clay loan soil. Keywords: Test, mechanization, precision agriculture.
• Ottman, M. J., Andrade-sanchez, P., & Andrade Sanchez, P. (2012). Determination of optimal planting configuration of low input and organic barley and wheat production in Arizona. The University of Arizona - Cooperative Extension.
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  Summary Markets for organic barley and wheat are expanding. A major problem growing organic barley and wheat is controlling the weeds. Organic barley and wheat were grown in conventional 6-inch drill spacing but also in 30 inch spacing so weeds could be cultivated in a study at the Larry Hart Farm near Maricopa. The weed pressure was moderate and the weed biomass was about 16 to 26% of the crop biomass near maturity. The primary weed was canarygrass and the secondary weed was malva. Grain yields of the wheat (durum) were similar regardless of row spacing, but the barley grain yields were 4327 lbs/acre in the 6 inch spacing and 3330 lbs/acre in the 30 inch spacing.
• Rossato, O. B., Andrade-Sanchez, P., Sebastiao, G., & Costa, C. (2012). Reflectance and fluorescence sensors to assess nitrogen levels, biomass production and yield of cotton. Pesquisa Agropecuaria Brasileira, 47(8), 1133-1141.
• Thorp, K., Andrade-Sanchez, P., Gore, M., White, J., & French, A. (2012). Information technologies for field-based high-throughput phenotyping. Resource: Engineering and Technology for Sustainable World, 19(5), 8-9.
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  Field-based, high-throughput phenotyping seeks to implement information technologies, including sensing and computing tools, to rapidly characterize the growth responses of genetically diverse plant populations in the field and relate these responses to individual genes. Field-based, high-throughput phenotyping includes a proximal sensing system, including a sensor platform and instrumentation, that can rapidly quantify phenotypic variation in the field. The instrument package includes four active radiometers for measuring canopy spectral reflectance in three wavebands, eight infrared thermometers for measuring canopy temperature, four sonar sensors that measure canopy height, and an RTK GPS receiver for horizontal and vertical positioning. Field-based, high-throughput phenotyping using only manual labor is prohibitive because thousands of distinct genetic lines must be characterized. 1.
• White, J. W., Andrade-Sanchez, P., Gore, M. A., Bronson, K. F., Coffelt, T. A., Conley, M. M., Feldmann, K. A., French, A. N., Heun, J. T., Hunsaker, D. J., Jenks, M. A., Kimball, B. A., Roth, R. L., Strand, R. J., Thorp, K. R., Wall, G. W., & Wang, G. (2012). Field-based phenomics for plant genetics research. Field Crops Research, 133, 101-112.
• Andrade Sanchez, P., & Heun, J. T. (2011). A General Guide to Global Positioning Systems (GPS). University of Arizona Cooperative Extension.
• Montanha, G. K., Guerra, S. P., Sanchez, P. A., Campos, F., & Lanças, K. P. (2011). CONSUMO DE COMBUSTÍVEL DE UM TRATOR AGRÍCOLA NO PREPARO DO SOLO PARA A CULTURA DO ALGODÃO IRRIGADO EM FUNÇÃO DA PRESSÃO DE INFLAÇÃO NOS PNEUS. ENERGIA NA AGRICULTURA, 26(1), 39-51. doi:10.17224/energagric.2011v26n1p39-51
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  Os resultados evidenciaram
• Andrade Sanchez, P., & Heun, J. T. (2010). Things to Know About Applying Precision Agriculture Technologies in Arizona. The University of Arizona - Cooperative Extension.
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  Precision Agriculture (PA) technologies can enhance the productivity of irrigated agriculture in Arizona. This guide is intended to aid growers in selecting the right technology when considering the need to acquire new, or upgrade existing equipment. It is expected that growers will adopt new technology only when it solves a problem in an economical way, therefore consultation with your local machinery dealer is a key step in being informed on issues such as cost, service, infrastructure requirements, and compatibility between components, systems, brands, etc.
• Andrade Sanchez, P., & Heun, J. T. (2010). Understanding Technical Terms and Acronyms Used in Precision Agriculture. The University of Arizona - Cooperative Extension.
• Andrade-Sanchez, P., Upadhyaya, S. K., Plouffe, C., & Poutre, B. (2008). Development and Field Evaluation of a Field-ready Soil Compaction Profile Sensor for Real-time Applications. ASABE - Applied Engineering in Agriculture, 24(6), 743-750.
• Sudduth, K. A., Chung, S., Andrade-Sanchez, P., & Upadhyaya, S. K. (2008). Field comparison of two prototype soil strength profile sensors. Computers and Electronics in Agriculture, 61(1), 20-31.
• Andrade-Sanchez, P., Upadhyaya, S. K., & Jenkins, B. M. (2007). Development, construction, and field evaluation of a soil compaction profile sensor. Transactions of the ASABE, 50(3), 719-725.
• Sakai, K., Andrade-Sanchez, P., & Upadhyaya, S. K. (2005). Periodicity and stochastic hierarchical orders of soil cutting force data detected by an "auto-regressive error distribution function" (AREF). Transactions of the ASAE, 48(6), 2039-2046.
• Sakai, K., Andrade-sanchez, P., & Upadhyaya, S. K. (2005). Periodicity and stochastic hierarchical orders of soil cutting force data detected by an "Auto-regressive error distribution function" (AREF). Transactions of the ASABE, 48(6), 2039-2046. doi:10.13031/2013.20080
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  In this article, we describe a methodology to estimate typical soil properties such as moisture content and compactness using soil cutting force fluctuation information obtained by a conventional chisel. The data were obtained in a Yolo loam field under four different soil conditions: tilled dry (TD), tilled wet (TW), untilled dry (UD), and untilled wet (UW). In order to quantify the complexity of fluctuating patterns of soil cutting force and soil physical properties, we introduce a new time-series analysis technique, the auto-regressive error function (AREF). We found that the frequency distribution pattern of the modified time-series data with the time lag showed a very clear shift with change in the time lag. The AREF was developed to detect this pattern shift. The soil cutting force time-series data obtained using an instrumented chisel were analyzed using power spectrum and AREF techniques. The spatial power spectrum analysis detected periodicity under dry soil conditions. On the other hand, the AREF showed a very clear hierarchical order, which was caused by the existence of self-similarity in the fluctuating patterns of soil cutting forces under all four tested soil conditions. Two AREF parameters were found to be related to soil moisture content and cone index, but not bulk density.
• Andrade-Sanchez, P., Upadhyaya, S. K., Aguera-Vega, J., & Jenkins, B. M. (2004). Evaluation of a capacitance-based soil moisture sensor for real-time applications. Transactions of the ASAE, 47(4), 1281-1287.
• Andrade-sanchez, P., Upadhyaya, S. K., Agueravega, J., & Jenkins, B. M. (2004). Evaluation of a capacitance-based soil moisture sensor for real-time applications. Transactions of the ASABE, 47(4), 1281-1287. doi:10.13031/2013.16562
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  A low resonant frequency, dielectric-based soil moisture sensor developed by Retrokool, Inc. (Berkeley, Cal.) was slightly modified and tested under static laboratory conditions using soil from three different series (Capay silty clay, Yolo loam, and Metz Variant fine sandy loam) of contrasting textural composition. The sensor response consisting of frequency and amplitude measurements was recorded over a range of volumetric moisture contents and salinity levels. The results indicated that the sensor was insensitive to changes in soil texture. The modification to the sensing circuit improved the moisture detection range for the sensor. However, the sensor response was influenced by changes in soil salinity. Empirical analyses showed that a normalized sensor output was highly correlated with the soil conductance. Under laboratory conditions, these estimated conductance values correlated well with soil moisture content (r2 = 0.87). When this sensor was vehicle-mounted behind a tillage tool and tested under field conditions in a Yolo loam soil, estimated conductance values were well correlated with measured soil moisture content (r2 = 0.78). The results suggest the sensor has good potential for routine applications in real-time measurement of soil moisture for precision agriculture applications.
• Andrade-Sanchez, P., Upadhyaya, S. K., & Sakai, K. (2003). Variability in Draft Data Observed During Tillage. 2003 ASABE Annual International Meeting. doi:10.13031/2013.15020
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  An instrumented tine was used to measure tillage force data in a Yolo loam field nearU.C.Davis campus under four different soil conditions tilled dry, tilled wet, untilled dry, and untilledwet. As expected, these data exhibit spatial variations that look like random fluctuations. However, itis possible that an underlying order exists in such data. Alternately, the data may be purelystochastic in nature. In any case, variability in the force data may be related to soil properties andprovide a technique to measure those parameters. The data were analyzed to determine if theunderlying phenomenon that caused the variability is purely stochastic or originated fromdeterministic chaos.

Proceedings Publications

• Thorp, K., Elshikha, D., & Andrade-Sanchez, P. (2021, December 2021). Irrigation Management Outcomes using Increasingly Complex Geospatial Technologies. In 6th Decennial National Irrigation Symposium.
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  Several geospatial technologies are now available for applications in precision irrigation, including soil property mapping, remote imaging from unmanned aerial systems (drones), spatial crop evapotranspiration modeling, and site-specific irrigation application technology. However, the potential contribution of different geospatial technologies toward improving crop production and water use efficiency remains unclear. The objective of this study was to determine agronomic outcomes using a cascade of increasingly complex geospatial technologies to assist irrigation decisions and applications. The four treatments from least to greatest complexity were 1) an FAO-56 water balance model with field-average soil data, 2) treatment #1 applied geospatially with site-specific soil information, 3) treatment #2 with FAO-56 basal crop coefficients (Kcb) estimated from weekly drone images, and 4) treatment #3 with irrigation applications occurring via commercial, map-based, site-specific irrigation technology. The field trial was conducted with cotton in the 2019 growing season at Maricopa, AZ. Results demonstrated no improvement in cotton fiber yield or irrigation water use efficiency by incorporating geospatial information into the FAO-56 water balance model. Fiber yield for the drone-based treatments were significantly lower than yield for less complex management technologies. The most positive outcome of the study was the development of an image processing pipeline to use drone-based images for irrigation decisions. Future irrigation management research in Arizona should develop technologies for improving temporal (rather than spatial) aspects of irrigation scheduling.
• Andrade-sanchez, P., Burnette, M., Fahlgren, N., Kooper, R., Lebauer, D., Maloney, J. D., Mockler, T. C., Newcomb, M., Rohde, G. S., Sagan, V., Shakoor, N., Sidike, P., Terstriep, J. A., Ward, R. W., & Willis, C. (2018, July 2018). TERRA-REF Data Processing Infrastructure. In PEARC '18: Practice and Experience in Advanced Research Computing, Article 27, 1-7.
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  The Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform (TERRA-REF) provides a data and computation pipeline responsible for collecting, transferring, processing and distributing large volumes of crop sensing and genomic data from genetically informative germplasm sets. The primary source of these data is a field scanner system built over an experimental field at the University of Arizona Maricopa Agricultural Center. The scanner uses several different sensors to observe the field at a dense collection frequency with high resolution. These sensors include RGB stereo, thermal, pulse-amplitude modulated chlorophyll fluorescence, imaging spectrometer cameras, a 3D laser scanner, and environmental monitors. In addition, data from sensors mounted on tractors, UAVs, an indoor controlled-environment facility, and manually collected measurements are integrated into the pipeline. Up to two TB of data per day are collected and transferred to the National Center for Supercomputing Applications at the University of Illinois (NCSA) where they are processed.In this paper we describe the technical architecture for the TERRA-REF data and computing pipeline. This modular and scalable pipeline provides a suite of components to convert raw imagery to standard formats, geospatially subset data, and identify biophysical and physiological plant features related to crop productivity, resource use, and stress tolerance. Derived data products are uploaded to the Clowder content management system and the BETYdb traits and yields database for querying, supporting research at an experimental plot level. All software is open source2 under a BSD 3-clause or similar license and the data products are open access (currently for evaluation with a full release in fall 2019). In addition, we provide computing environments in which users can explore data and develop new tools. The goal of this system is to enable scientists to evaluate and use data, create new algorithms, and advance the science of digital agriculture and crop improvement.
• Bronson, K. F., Norton, E. R., Andrade Sanchez, P., Hunsaker, D., Williams, C., & Thorp, K. (2017, January). Improving Nitrogen Management for Subsurface Drip Irrigated Cotton in Arizona. In 2017 Beltwide Cotton Conferences, 147-149.
• Zhang, Q., Upadhyaya, S. K., Liao, Q., & Andrade-sanchez, P. (2017). Finite element modeling and Simulations to investigate the relationship between the cone index profile and draft requirements of a compaction profile sensor with depth. In 2017 Spokane, Washington July 16 - July 19, 2017.
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  Previous research conducted using a compaction profile sensor and a standard cone penetrometer over a wide range of soil types and conditions found that the unit pressure acting on the cutting edge, defined as the cone index equivalent (CIE), at a specific depth (d) was related to the cone index (CI) value at that depth, the depth of the cutting edge (d), and the interaction between CI and the depth of the cutting edge (i.e., CI x d) with a very high coefficient of multiple determination irrespective of the soil type and conditions. The objective of this study was to provide an analytical basis for the relationship between CIE and CI. A two-dimensional axisymmetric model for soil-cone interaction and a three-dimensional model for soil-tine interaction were developed using a finite element method (FEM). A non-linear elasto-plastic constitutive behavior along with the Drucker-Prager yield criterion were used to represent the soil cutting process. Simulations studies were conducted in 25 distinct soil types and conditions, and the results indicated a similar relationship between CIE and CI, as observed in the previous research. These results support the existence of a strong theoretical basis for the empirical relationship observed in the previous research.
• Bronson, K., Norton, E. R., Hunsaker, D., & Andrade Sanchez, P. (2015, January). Improving Nitrogen fertilizer management for overhead sprinkler-irrigated cotton in the Western US. In 2015 Beltwide Cotton Conference, 403-410.
• Bronson, K., Norton, E. R., Hunsaker, D., & Lidell, E. (2015, January). Updating petiole Nitrate-based N fertilizer recommendations for Arizona cotton. In 2015 Beltwide Cotton Conference, 392-397.
• Griffin, T., Barnes, E. M., Andrade-sanchez, P., Balkcom, K. S., Bauer, P. J., Bronson, K. F., Buschermohle, M. J., Ge, Y., Roberson, G. T., Taylor, R. K., Tubana, B., Varco, J. J., Yin, X., Vories, E. D., Vellidis, G., Allen, P. A., Wilkerson, J. B., Jones, A. P., Barber, L. T., & Arnall, D. B. (2014). Pooled Analysis of Combined Primary Data across Multiple States and Investigators for the Development of a NDVI-Based On-the-Go Nitrogen Application Algorithm for Cotton. In 2014 ASABE Annual International Meeting.
• Andrade-sanchez, P., & Heun, J. T. (2013). Sensor-based estimation of cotton plant height: Potential for site-specific plant growth management. In 2013 Kansas City, Missouri, July 21 - July 24, 2013.
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  Abstract. Current crop production technology provides control of chemical application of production inputs with exceptional field resolution, positioning accuracy, and the capability to generate real-time data to track machine efficiency, input use, ambient conditions, etc. To take full advantage of the real-time application control capabilities of modern technology, these control systems must be interfaced with electronic sensors to monitor plant conditions on-the go. In contrast to the level of sophistication in hardware and software used in application control, the development of soil/plant sensors still lags behind. Experimental work at the University of Arizona has shown the potential of displacement sensors and GPS-RTK systems to generate plant-height (PH) information from moving platforms. Sensor analog signals and GPS field elevation data were combined using an algorithm developed specifically to estimate plant height. Field trials in Maricopa, AZ in the Summer of 2012, included varietal differences (columnar and bushy types), planting densities (25, 50, and 75k plants/acre), and irrigation management (75 and 50% of water depletion) with measurements taken during the period of rapid growth. Data analysis is currently under way to compare sensor-based PH data with manual measurements. An extension of the experimental work is being directed at generating real-time data of cotton height:node ratio (H:N), by estimating the number of nodes as a function of heat-unit accumulation. Discussion of technology capable of generating sensor-based PH data includes measurement accuracy, performance parameters such as machine field capacity, and integration to control systems. Cotton H:N information has been shown to be a figure of merit for management of plant growth regulators (PGR), therefore technological development in this area can lead to automatic variable-rate application algorithms of plant growth regulators which will improve plant growth management of irrigated cotton.
• Andrade-sanchez, P., Bautista, E., French, A. W., Heun, J. T., Hunsaker, D. J., Royer, P. D., Thorp, K. R., & Waller, P. (2010, December 2010). Spatial Estimation of Crop Evapotranspiration, Soil Properties, and Infiltrated Water for Scheduling Cotton Surface Irrigations. In 5th National Decennial Irrigation Conference Proceedings, 5-8 December 2010, Phoenix Convention Center, Phoenix, Arizona USA, 2, 748-760.
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  Estimates of spatially distributed crop evapotranspiration (ETc) over large fields could be particularly valuable for aiding irrigation management decisions in arid regions where surface irrigation systems are predominant. The objectives are to evaluate an irrigation scheduling approach that combines remote sensing inputs with field data to provide fine-scale, spatial monitoring of crop water use and soil water status within surface-irrigated fields. Remote sensing observations of vegetation index were used to spatially estimate basal crop coefficients within 4-m x 8-m zones within borders of a 4.9-ha cotton field. These data were used to compute ETc within zones using FAO-56 procedures. Spatial inputs of soil properties were estimated from a ground-based apparent soil electrical conductivity survey. Spatial distribution of infiltrated water along the furrow was estimated using hydraulic field measurements and irrigation simulation software. An existing daily time-step, soil water balance computer program was modified to incorporate the spatial information and provide simultaneous monitoring of crop and soil conditions in zones. Irrigation scheduling using the spatial monitoring approach compared favorably in yield to traditional cotton irrigation scheduling used in the area, but reduced water use by 7 to 9%, whereas it attained as much as 19% higher yield compared to scheduling based on assuming a uniform crop coefficient for all zones. Managing water for large surface-irrigated fields aided by decision support tools and approaches that allow spatial monitoring of crop water use and soil conditions could improve precision and timing of irrigation water scheduling.
• Andrade-sanchez, P., Heun, J. T., Wang, G., & Zarnstorff, M. (2009). Characterizing the Response of Irrigated Cotton to Hail Damage through Canopy Reflectance Measurements in Arizona. In 2009 Reno, Nevada, June 21 - June 24, 2009.
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  Cotton production in Arizona can experience hail damage in the summer in a time of very active growth. The current method of loss assessment is based on visual inspection that relies on the experience of the insurance adjuster. Through the use of sensor technology, the evaluation system can be greatly improved in the areas of spatial coverage and standardized analysis, with significant time and cost savings. This paper describes research carried out in central Arizona in irrigated cotton during the 2008 growing season. The goal of this project was to characterize through canopy reflectance measurements the crop response to hail damage simulated by manual branch removal. The treatments included a control and 25, 50, 75, and 100% removal of fruiting branches at three growth stages: 0, 14, and 28 days after flowering. The instrumentation included a 16-channel radiometer manufactured by CropScan programmed to scan in a range from 460 to 880 nm. After branch removal treatments, the canopy was scanned up to four times in a time period of 20 days. Preliminary results show that plants responded to the intensity of branch removal with different growth rates. Pending yield data will be added to the final analysis.
• Oguri, G., Andrade-sanchez, P., & Heun, J. T. (2009). Potential use of the veris apparent EC sensor to predict soil texture under the semi-arid conditions of central Arizona. In 2009 Reno, Nevada, June 21 - June 24, 2009, 4034-4041.
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  The operational details of the apparent electrical conductivity (ECa) sensor manufactured by Veris Technologies have been extensively documented in literature reports, but the geographical distribution of these research studies indicate a strong regional concentration in the US Mid-west and Southern states. The agricultural lands of these states diverge significantly to the soil conditions and water regime of irrigated land in the US South-western states such as Arizona where there is no previous research reports of the use of this particular sensor. The objectives of the present study were to analyze the performance of this sensor under the conditions of typical soils in irrigated farms of Central Arizona. We tested under static conditions the performance of the sensor on three soils of contrasting texture. Observations were collected as time series data as soil moisture changed from saturation to permanent wilting point. Observations were repeated at the hours of lowest and highest temperatures. In addition, this study included soil penetration resistance and salinity determinations. Preliminary results indicate that soil temperature of the upper layer caused the most dynamic change in the sensor output. The ECa curves of the three soil textures tested had well defined distinctive characteristics. Final multivariate analysis is pending.
• Andrade-sanchez, P., Upadhyaya, S. K., Plouffe, C., & Poutre, B. (2008). Potential Use of the UCDavis Soil Compaction Profile Sensor (UCD-SCPS) for Site-Specific Tillage Applications. In 2008 Providence, Rhode Island, June 29 - July 2, 2008.
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  The UC Davis Soil compaction sensor (UCD-SCPS) is designed to quantify soil mechanical strength in a continuous way. This sensor consists of an instrumented shank, a data acquisition system and a DGPS receiver. The UCD-SCPS sensor is attached to a frame that is mounted on the 3-point hitch of a farm tractor and pulled through the field engaging the soil in a way similar to a commercial ripper shank. This sensor can provide data related to the soil profile up to a depth of 460 mm using a set of five force transducers. The instrumented blade has a thickness of 29 mm.
• Jenkins, B. M., Bakker, R. R., Williams, R. B., Bakker-dhaliwal, R., Summers, S., Lee, H., Bernheim, L. G., Huisman, W., Yan, L., Andrade Sanchez, P., & Yore, M. W. (2000). Commercial feasibility of utilizing rice straw in power generation. In Bioenergy 2000.

Presentations

• Sanchez, C. A., & Andrade Sanchez, P. (2024, March). Spatial and temporal N management for irrigated vegetable production systems. Fertilizer Research Forum. Fort Worth TX: FERT FOUNDATION.
• Andrade Sanchez, P. (2022, February). Variable-rate input application in SW agriculture: Great potential benefits + outstanding technology + lagging science.. Southwest Ag Summit. Arizona Western College. 2020 S Ave 8 E, Yuma, AZ 85365.
• Andrade Sanchez, P. (2022, November). In-field soil variability in the Yuma area: Tools to characterize it and implementation of variable management technologies.. Desert Agriculture Research Symposium. Pivot Point Conference Center, 200 S. Madison Ave, Yuma, AZ 85364: YCEDA.
• Norton, E. R., & Andrade Sanchez, P. (2014, June). New Apps for Commercial Agriculture. New Technologies Conference. Maricopa, AZ: The University of Arizona.
• Andrade Sanchez, P., & Heun, J. (2013, July/2013). Sensor-based estimation of cotton plant height: Potential for site-specific plant growth management. 2013 ASABE International Meeting. Kansas City, Missouri: American Society of Agricultural and Biological Engineers.

Poster Presentations

• Ottman, M. J., Norton, E. R., Mostafa, A. M., Andrade Sanchez, P., Grijalva, P., Diaz, D., & Evancho, B. E. (2020, October). Agronomic Assessment of Corn Silage Forage Production in Buckeye, and Casa Grande Arizona: Part of the Arizona Dairy Forage Initiative. Arizona Cooperative Extension 2020 Virtual Conference. Virtual: Arizona Cooperative Extension.
• Norton, R., Ottman, M. J., Evancho, B. E., Mostafa, A. M., Andrade Sanchez, P., & Diaz, D. (2019, April). Arizona Extension Dairy Forage Initiative: Agronomic Assessment of Common Forage Commodities in the State of Arizona. 2019 ALVSCE Poster Forum. Tucson Arizona.
• Ottman, M. J., Diaz, D., Norton, R., Andrade Sanchez, P., Evancho, B. E., Mostafa, A. M., Mostafa, A. M., Evancho, B. E., Norton, R., Andrade Sanchez, P., Diaz, D., & Ottman, M. J. (2019, August). Evaluation of agronomic needs to improve our extension educational programs to dairy forage growers in the state.. 2019 Arizona Cooperative Extension Conference. Tucson Arizona: University of Arizona Cooperative Extension.

Others

• Andrade Sanchez, P., Walker, K. R., Brophy, M., Nair, S., Li, S., & Gouge, D. H. (2020, October). Mosquitoes (English Trifold Quick Read). University of Arizona Cooperative Extension, AZ1873. https://extension.arizona.edu/sites/extension.arizona.edu/files/pubs/az1873-2021.pdf
• Andrade Sanchez, P., Fournier, A. J., Walker, K. R., Nair, S., Gouge, D. H., & Li, S. (2018, Septiembre). Lo que debe saber sobre los repelentes de mosquitos y garrapatas. Extensión Cooperativa de la Universidad de Arizona, corto de MIP. https://cals.arizona.edu/apmc/docs/Repellents-IPMShort-Spanish.pdf