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
• Mitra, S., Roselius, M., Andrade-Sanchez, P., 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(Issue 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 majority of our evaluated queries and (Formula presented.) 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
• Heun, J. T., Attalah, S., French, A. N., Lehner, K. R., McKay, J. K., Mullen, J. L., Ottman, M. J., & Andrade-Sanchez, P. (2019). Deployment of lidar from a ground platform: Customizing a low-cost, information-rich and user-friendly application for field phenomics research. Sensors (Switzerland), 19(Issue 24). doi:10.3390/s19245358
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  Using sensors and electronic systems for characterization of plant traits provides valuable digital inputs to support complex analytical modeling in genetics research. In field applications, frequent sensor deployment enables the study of the dynamics of these traits and their interaction with the environment. This study focused on implementing lidar (light detection and ranging) technology to generate 2D displacement data at high spatial resolution and extract plant architectural parameters, namely canopy height and cover, in a diverse population of 252 maize (Zea mays L.) genotypes. A prime objective was to develop the mechanical and electrical subcomponents for field deployment from a ground vehicle. Data reduction approaches were implemented for efficient same-day post-processing to generate by-plot statistics. The lidar system was successfully deployed six times in a span of 42 days. Lidar data accuracy was validated through independent measurements in a subset of 75 experimental units. Manual and lidar-derived canopy height measurements were compared resulting in root mean square error (RMSE) = 0.068 m and r2 = 0.81. Subsequent genome-wide association study (GWAS) analyses for quantitative trait locus (QTL) identification and comparisons of genetic correlations and heritabilities for manual and lidar-based traits showed statistically significant associations. Low-cost, field-ready lidar of computational simplicity make possible timely phenotyping of diverse populations in multiple environments.
• Thompson, A. L., Thorp, K. R., Conley, M. M., Elshikha, D. M., French, A. N., Andrade-Sanchez, P., & Pauli, D. (2019). Comparing nadir and multi-angle view sensor technologies for measuring in-field plant height of upland cotton. Remote Sensing, 11(Issue 6). doi:10.3390/rs11060700
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  Plant height is a morphological characteristic of plant growth that is a useful indicator of plant stress resulting from water and nutrient deficit. While height is a relatively simple trait, it can be difficult to measure accurately, especially in crops with complex canopy architectures like cotton. This paper describes the deployment of four nadir view ultrasonic transducers (UTs), two light detection and ranging (LiDAR) systems, and an unmanned aerial system (UAS) with a digital color camera to characterize plant height in an upland cotton breeding trial. The comparison of the UTs with manual measurements demonstrated that the Honeywell and Pepperl+Fuchs sensors provided more precise estimates of plant height than the MaxSonar and db3 Pulsar sensors. Performance of the multi-angle view LiDAR and UAS technologies demonstrated that the UAS derived 3-D point clouds had stronger correlations (0.980) with the UTs than the proximal LiDAR sensors. As manual measurements require increased time and labor in large breeding trials and are prone to human error reducing repeatability, UT and UAS technologies are an efficient and effective means of characterizing cotton plant height.
• Condorelli, G. E., Maccaferri, M., Newcomb, M., Andrade-Sanchez, P., White, J. W., French, A. N., Sciara, G., Ward, R., & Tuberosa, R. (2018). Corrigendum: Comparative aerial and ground based high throughput phenotyping for the genetic dissection of NDVI as a proxy for drought adaptive traits in durum wheat (Front. Plant Sci. 9, 893, 10.3389/fpls.2018.00893). Frontiers in Plant Science, 9(Issue). doi:10.3389/fpls.2018.01885
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  A Corrigendum on Comparative Aerial and Ground Based High Throughput Phenotyping for the Genetic Dissection of NDVI as a Proxy for Drought Adaptive Traits in Durum Wheat by Condorelli, G. E., Maccaferri, M., Newcomb, M., Andrade-Sanchez, P., White, J. W., French, A. N., et al. (2018). Front. Plant Sci. 9:893. doi: 10.3389/fpls.2018.00893 In the original article, we neglected to include the acknowledgment of the TERRA REF project, funded by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under award number DE-AR0000594. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
• 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.
• Bronson, K. F., Hunsaker, D. J., Mon, J., White, J. W., Conley, M. M., Thorp, K. R., Bautista, E., Andrade-Sanchez, P., & 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(Issue 6). doi:10.2136/sssaj2017.07.0225
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  Nitrogen fertilizer use efficiency (NUE) is low in surface-irrigated cotton (Gossypium hirsutum L.), especially when adding N to irrigation water. A NO3 soil-test algorithm was compared with canopy reflectance-based N management with surface- overhead sprinkler-irrigation in central Arizona. The surface irrigation studies also compared fertigation of N fertilizer with knifing- in of N and the addition of a urease and nitrification inhibitor (Agrotain Plus, Koch Agronomic Services, Wichita, KS) to urea ammonium nitrate (UAN). Cotton lint and seed yields responded positively to N fertilizer in all four site-years. Recovery efficiency (RE) of N at low N fertilizer rates (60 to 76 kg N ha-1) ranged from 21 to 61% with surface irrigation and from 81 to 97% with overhead sprinkler irrigation. Deep percolation below 1.8 m was 4 to 11% of applied surface irrigations and rain, but was undetectable in the overhead sprinkler. Leaching of NO3 was apparently the largest N loss pathway in the surface-irrigated system. Fertigating UAN into surface irrigation resulted in similar lint yields and RE as knifing UAN. Use of Agrotain Plus with UAN gave similar yields and RE as using UAN alone. Reflectance-based N management using normalized difference vegetation index-amber (NDVIA) saved 50% of N fertilizer of the full soil-test based dose without a yield reduction in three of four site- years. Nitrogen fertilizer was over-prescribed with the soil-test-based treatment. This may have been due to not accounting for N mineralization, which the reflectance method indirectly measures.
• Pauli, D., White, J. W., Andrade-Sanchez, P., Conley, M. M., Heun, J., Thorp, K. R., French, A. N., Hunsaker, D. J., Carmo-Silva, E., Wang, G., & Gore, M. A. (2017). Investigation of the influence of leaf thickness on canopy reflectance and physiological traits in upland and pima cotton populations. Frontiers in Plant Science, 8(Issue). doi:10.3389/fpls.2017.01405
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  Many systems for field-based, high-throughput phenotyping (FB-HTP) quantify and characterize the reflected radiation fromthe crop canopy to derive phenotypes, as well as infer plant function and health status. However, given the technology’s nascent status, it remains unknown how biophysical and physiological properties of the plant canopy impact downstream interpretation and application of canopy reflectance data. In that light, we assessed relationships between leaf thickness and several canopy-associated traits, including normalized difference vegetation index (NDVI), which was collected via active reflectance sensors carried on a mobile FB-HTP system, carbon isotope discrimination (CID), and chlorophyll content. To investigate the relationships among traits, two distinct cotton populations, an upland (Gossypium hirsutum L.) recombinant inbred line (RIL) population of 95 lines and a Pima (G. barbadense L.) population composed of 25 diverse cultivars, were evaluated under contrasting irrigation regimes, water-limited (WL) and well-watered (WW) conditions, across 3 years. We detected four quantitative trait loci (QTL) and significant variation in both populations for leaf thickness among genotypes as well as high estimates of broad-sense heritability (on average, above 0.7 for both populations), indicating a strong genetic basis for leaf thickness. Strong phenotypic correlations (maximum r = −0.73) were observed between leaf thickness and NDVI in the Pima population, but not the RIL population. Additionally, estimated genotypic correlations within the RIL population for leaf thickness with CID, chlorophyll content, and nitrogen discrimination (rgij = −0.32, 0.48, and 0.40, respectively) were all significant under WW but not WL conditions. Economically important fiber quality traits did not exhibit significant phenotypic or genotypic correlations with canopy traits. Overall, our results support considering variation in leaf thickness as a potential contributing factor to variation in NDVI or other canopy traits measured via proximal sensing, and as a trait that impacts fundamental physiological responses of plants.
• 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).
• 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(Issue 3). doi:10.1063/1.4978027
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  Real-world processes are often combinations of deterministic and stochastic processes. Soil failure observed during farm tillage is one example of this phenomenon. In this paper, we investigated the nonlinear features of soil failure patterns in a farm tillage process. We demonstrate emerging determinism in soil failure patterns from stochastic processes under specific soil conditions. We normalized the deterministic nonlinear prediction considering autocorrelation and propose it as a robust way of extracting a nonlinear dynamical system from noise contaminated motion. Soil is a typical granular material. The results obtained here are expected to be applicable to granular materials in general. From a global scale to nano scale, the granular material is featured in seismology, geotechnology, soil mechanics, and particle technology. The results and discussions presented here are applicable in these wide research areas. The proposed method and our findings are useful with respect to the application of nonlinear dynamics to investigate complex motions generated from granular materials.
• 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
• 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(Issue 6). doi:10.13031/trans.12323
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  Crop growth simulation models can address a variety of agricultural problems, but their use to directly assist in-season irrigation management decisions is less common. Confidence in model reliability can be increased if models are shown to provide improved in-season management recommendations, which are explicitly tested in the field. The objective of this study was to compare the CSM-CROPGRO-Cotton model (with recently updated ET routines) to a well-tested FAO- 56 irrigation scheduling spreadsheet by (1) using both tools to schedule cotton irrigation during 2014 and 2015 in central Arizona and (2) conducting a post-hoc simulation study to further compare outputs from these tools. Two replications of each irrigation scheduling treatment and a water-stressed treatment were established on a 2.6 ha field. Irrigation schedules were developed on a weekly basis and administered via an overhead lateral-move sprinkler irrigation system. Neutron moisture meters were used weekly to estimate soil moisture status and crop water use, and destructive plant samples were routinely collected to estimate cotton leaf area index (LAI) and canopy weight. Cotton yield was estimated using two mechanical cotton pickers with differing capabilities: (1) a two-row picker that facilitated manual collection of yield samples from 32 m2 areas and (2) a four-row picker equipped with a sensor-based cotton yield monitoring system. In addition to statistical testing of field data via mixed models, the data were used for post-hoc reparameterization and fine-tuning of the irrigation scheduling tools. Post-hoc simulations were conducted to compare measured and simulated evapotranspiration, crop coefficients, root zone soil moisture depletion, cotton growth metrics, and yield for each irrigation treatment. While total seasonal irrigation amounts were similar among the two scheduling tools, the crop model recommended more water during anthesis and less during the early season, which led to higher cotton fiber yield in both seasons (p < 0.05). The tools calculated cumulative evapotranspiration similarly, with root mean squared errors (RMSEs) less than 13%; however, FAO- 56 crop coefficient (Kc) plots demonstrated subtle differences in daily evapotranspiration calculations. Root zone soil moisture depletion was better calculated by CSM-CROPGRO-Cotton, perhaps due to its more complex soil profile simulation; however, RMSEs for depletion always exceeded 20% for both tools and reached 149% for the FAO-56 spreadsheet in 2014. CSM-CROPGRO-Cotton simulated cotton LAI, canopy weight, canopy height, and yield with RMSEs less than 21%, while the FAO-56 spreadsheet had no capability for such outputs. Through field verification and thorough post-hoc data analysis, the results demonstrated that the CSM-CROPGRO-Cotton model with updated FAO-56 ET routines could match or exceed the accuracy and capability of an FAO-56 spreadsheet tool for cotton water use calculations and irrigation scheduling.
• 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.
• 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(Issue 4). doi:10.1534/g3.115.023515
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  The application of high-throughput plant phenotyping (HTPP) to continuously study plant populations under relevant growing conditions creates the possibility to more efficiently dissect the genetic basis of dynamic adaptive traits. Toward this end, we employed a field-based HTPP system that deployed sets of sensors to simultaneously measure canopy temperature, reflectance, and height on a cotton (Gossypium hirsutum L.) recombinant inbred line mapping population. The evaluation trials were conducted under well-watered and water-limited conditions in a replicated field experiment at a hot, arid location in central Arizona, with trait measurements taken at different times on multiple days across 2010-2012. Canopy temperature, normalized difference vegetation index (NDVI), height, and leaf area index (LAI) displayed moderate-to-high broad-sense heritabilities, as well as varied interactions among genotypes with water regime and time of day. Distinct temporal patterns of quantitative trait loci (QTL) expression were mostly observed for canopy temperature and NDVI, and varied across plant developmental stages. In addition, the strength of correlation between HTPP canopy traits and agronomic traits, such as lint yield, displayed a time-dependent relationship. We also found that the genomic position of some QTL controlling HTPP canopy traits were shared with those of QTL identified for agronomic and physiological traits. This work demonstrates the novel use of a field-based HTPP system to study the genetic basis of stress-adaptive traits in cotton, and these results have the potential to facilitate the development of stress-resilient cotton cultivars.
• 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.
• 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(Issue 3). doi:10.1111/jac.12142
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  Hail storm damage to the cotton (Gossypium hirsutum L.) plants can destroy vegetative and reproductive structures, modify canopy architecture and impact lint yield. Field studies were conducted at University of Arizona Maricopa Agricultural Center in 2011, 2012 and 2013 to examine cotton plant architecture changes and compensatory growth in response to removal treatments of uppermost nodes on main stem (terminal bud removal, 2 node removal and 4 node removal) as simulation of hail damage at the node 2, 4, 8, 12, 16 and 24 growth stages. Main stem node removal caused significant decrease in leaf area and biomass, especially at early growth stages. However, significant lint yield reduction only occurred by removing 2 nodes at the node 4 stage and removing 4 nodes at the node 8 stage in 2011, removing terminal bud at the node 12 stage in 2012 and removing terminal bud, 2 nodes and 4 nodes at the node 8 stage in 2013. The lint yield reduction did not exceed 13 % in all three growing seasons. Yield loss due to main stem node removal was mainly compensated by increased boll number on the vegetative branches at early growth stages and on fruiting branches at late growth stages. Yield compensation from vegetative branches increased with number of main stem nodes removed. This study suggests that the cotton crop has a strong compensatory ability to plant structure damage due to its indeterminate growth and longer growing season in the region.
• 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(Issue). doi:10.1016/j.agwat.2015.06.016
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  The use of irrigation scheduling tools to produce cotton under-surface irrigation in the arid southwestern USA is minimal. In the State of Arizona, where traditional irrigation scheduling is the norm, producers use an average of 1460. mm annually to grow a cotton crop. The purpose of this paper was to determine whether or not the use of ET-based irrigation scheduling methods could improve lint yield and irrigation water use productivity over traditional cotton border irrigation scheduling practices in the region. A field study with four irrigation scheduling treatments replicated in 4 blocks was conducted for two cotton seasons (2009 and 2011) in 16, 12-m × 168-m cotton borders at the Maricopa Agricultural Center (MAC), in Arizona, USA. Remotely-sensed vegetation indices (VI) were used to estimate basal crop coefficients (Kcb) at 40, 4-m × 8-m zones within borders for two treatments, denoted as VI_A and VI_B, whereas a single Kcb curve was applied to all zones in borders for a third treatment (FAO). Daily ETc for these three treatments was estimated using FAO-56 dual crop coefficient procedures with local weather data and irrigation scheduling for the three treatments were based on soil water balance predictions of soil water depletion (SWD). For the VI_A and FAO treatments, irrigations were given when predicted SWD of all 160 zones in the treatment averaged 45% of total available water (TAW). For the VI_B treatment, irrigations were given when 5% of the 160 zones in the treatment were predicted to be at 65% SWD. A fourth treatment (MAC) represented the traditional irrigation scheduling treatment and was scheduled solely by the MAC farm irrigation manager using only experience as a guide. The study showed that the lint yields attained under the MAC farm manager's irrigation scheduling equaled or exceeded the yields for the three ET-based irrigation scheduling treatments. Although the MAC irrigation scheduling resulted in somewhat higher irrigation input than for the other treatments, the MAC treatment maintained or exceeded the irrigation water productivity attained for other treatments that had lower irrigation inputs. A major conclusion of the study was that present-day irrigation water use for cotton in surface-irrigated fields could be substantially reduced. When compared to Arizona state cotton averages, any of the four treatments presented in the study could potentially offer methods to significantly reduce cotton irrigation water use while maintaining or increasing current lint yields levels.
• 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(Issue). doi:10.1016/j.compag.2015.09.005
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  Field-based plant phenomics requires robust crop sensing platforms and data analysis tools to successfully identify cultivars that exhibit phenotypes with high agronomic and economic importance. Such efforts will lead to genetic improvements that maintain high crop yield with concomitant tolerance to environmental stresses. The objectives of this study were to investigate proximal hyperspectral sensing with a field spectroradiometer and to compare data analysis approaches for estimating four cotton phenotypes: leaf water content (Cw), specific leaf mass (Cm), leaf chlorophyll a+b content (Cab), and leaf area index (LAI). Field studies tested 25 Pima cotton cultivars grown under well-watered and water-limited conditions in central Arizona from 2010 to 2012. Several vegetation indices, including the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the physiological (or photochemical) reflectance index (PRI) were compared with partial least squares regression (PLSR) approaches to estimate the four phenotypes. Additionally, inversion of the PROSAIL plant canopy reflectance model was investigated to estimate phenotypes based on 3.68 billion PROSAIL simulations on a supercomputer. Phenotypic estimates from each approach were compared with field measurements, and hierarchical linear mixed modeling was used to identify differences in the estimates among the cultivars and water levels. The PLSR approach performed best and estimated Cw,Cm,Cab, and LAI with root mean squared errors (RMSEs) between measured and modeled values of 6.8%, 10.9%, 13.1%, and 18.5%, respectively. Using linear regression with the vegetation indices, no index estimated Cw,Cm,Cab, and LAI with RMSEs better than 9.6%, 16.9%, 14.2%, and 28.8%, respectively. PROSAIL model inversion could estimate Cab and LAI with RMSEs of about 16% and 29%, depending on the objective function. However, the RMSEs for Cw and Cm from PROSAIL model inversion were greater than 30%. Compared to PLSR, advantages to the physically-based PROSAIL model include its ability to simulate the canopy's bidirectional reflectance distribution function (BRDF) and to estimate phenotypes from canopy spectral reflectance without a training data set. All proximal hyperspectral approaches were able to identify differences in phenotypic estimates among the cultivars and irrigation regimes tested during the field studies. Improvements to these proximal hyperspectral sensing approaches could be realized with a high-throughput phenotyping platform able to rapidly collect canopy spectral reflectance data from multiple view angles.
• 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(Issue). doi:10.1016/j.fcr.2013.02.005
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  Plant architectural characteristics directly affect inter-plant interactions and cotton crop management. Field studies were conducted in the 2010 and 2011 growing seasons at the University of Arizona Maricopa Agricultural Center to investigate the influence of plant architecture on the crop growth, biomass partitioning, and lint yield response of upland cotton to population density under irrigated desert conditions. Two cotton varieties of contrasting architecture, a tall stature columnar type, Delta and Pine 164B2RF (DP164), and a short stature bush type, Stoneville 4498B2F (ST4498), were evaluated under three plant population densities: low density (LD, 57,300-66,500 plantsha-1), medium density (MD, 77,300-109,800 plantsha-1), and high density (HD, 126,700-146,000 plantsha-1). While high population densities significantly increased plant height of both varieties at early growth stages, plant height at late growth stages were not significantly different. High plant population reduced main stem node number of both varieties. However, the effect was more pronounced with ST4498 where the HD treatment had 2.9 and 2.5 nodes less than the LD treatments in 2010 and 2011, respectively. Plant population affected the height to node ratio of DP164 more than ST4498. The taller, columnar type DP164 partitioned more biomass into stems than the bush type ST4498 at all three plant populations. The LD treatment significantly reduced cotton growth and yield of DP164 in 2010. This study suggests the existence of differences in responses to plant population density among upland cotton varieties, attributable to canopy architecture. Additionally, results indicate that shorter bush type varieties might have higher growth plasticity and thus accommodate a broader range of plant densities than taller stature columnar type varieties. © 2013 Elsevier B.V.
• 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 CO 2 availability and inactivation of Rubisco limit photosynthesis in cotton plants under heat and drought stress in the field. Environmental and Experimental Botany, 83(Issue). doi:10.1016/j.envexpbot.2012.04.001
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  Heat and drought stresses are often coincident and constitute major factors limiting global crop yields. A better understanding of plant responses to the combination of these stresses under production environments will facilitate efforts to improve yield and water use efficiencies in a climatically changing world. To evaluate photosynthetic performance under dry-hot conditions, four cotton (Gossypium barbadense L.) cultivars, Monseratt Sea Island (MS), Pima 32 (P32), Pima S-6 (S6) and Pima S-7 (S7), were studied under well-watered (WW) and water-limited (WL) conditions at a field site in central Arizona. Differences in canopy temperature and leaf relative water content under WL conditions indicated that, of the four cultivars, MS was the most drought-sensitive and S6 the most drought-tolerant. Net CO 2 assimilation rates (A) and stomatal conductances (gs) decreased and leaf temperatures increased in WL compared to WW plants of all cultivars, but MS exhibited the greatest changes. The response of A to the intercellular CO 2 concentration (A-C i) showed that, along with stomatal closure, non-stomatal factors associated with heat stress also limited A under WL conditions, especially in MS. The activation state of ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) decreased in WL compared to WW plants, consistent with thermal inhibition of Rubisco activase activity. The extent of Rubisco deactivation could account for the metabolic limitation to photosynthesis in MS. Taken together, these data reveal the complex relationship between water availability and heat stress for field-grown cotton plants in a semi-arid environment. Both diffusive (drought-stress-induced) and biochemical (heat-stress-induced) limitations contributed to decreased photosynthetic performance under dry-hot conditions. © 2012.
• 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., Guerra, S. P., & Crusciol, C. A. (2012). Reflectance and fluorescence sensors to assess nitrogen levels, biomass production and yield of cotton. Pesquisa Agropecuaria Brasileira, 47(Issue 8). doi:10.1590/s0100-204x2012000800014
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  The objective of this work was to evaluate the potential of using reflectance and fluorescence sensors to assess the levels of N-NO3- in the petiole, plant biomass production and yield of cotton. A randomized complete block design was used in a 3×4 factorial arrangement, with four replicates. Treatments consisted of three cotton varieties (ST-4288-B2RF, ST-4498-B2RF and DP-164-B2RF) and of four N rates (0, 45, 90 and 135 kg ha-1). At 120 days after sowing, readings were done with optical sensors for canopy fluorescence and reflectance. There were no significant correlations for N-NO3-in the petiole with the reflectance sensor indices; however, there was correlation to biomass production (0.39) and yield (0.32 to 0.41). The fluorescence sensor indices were significantly correlated to N-NO3- in the petiole (0.34 to 0.61), biomass production (0.30 to 0.53) and yield (0.34). Compared to the reflectance indices, the fluorescence ones have a greater ability to assess the levels of N-NO3-in the petiole, a similar ability to detect' variation of plant biomass, and a lower ability to detect the variation in cotton yield when increasing rates of nitrogen are applied.
• 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. O., Andrade-Sanchez, P., & Upadhyaya, S. K. (2008). Field comparison of two prototype soil strength profile sensors. Computers and Electronics in Agriculture, 61(Issue 1). doi:10.1016/j.compag.2006.11.006
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  Soil compaction that is induced by tillage and traction is an ongoing concern in crop production, and also has environmental consequences. Although cone penetrometers provide standardized compaction measurements, the pointwise data collected makes it difficult to obtain enough data to represent within-field variability. Moreover, penetrometer data exhibit considerable variability even at a single location, requiring several measurements to obtain representative readings. For more efficient data collection, on-the-go compaction sensors that obtain data at multiple depths are being developed by several research groups. The objective of this research was to evaluate and compare the field performance of two on-the-go compaction sensors. Tests were conducted at two central Missouri field sites where soil types ranged from sandy loam to clay. The soil strength profile sensor (SSPS) measured compaction to a 50-cm depth on 10-cm intervals. The soil compaction profile sensor (SCPS) also used five sensing elements and obtained data to 40.6 cm on a 7.6-cm interval. Cone penetrometer measurements of compaction were obtained at intervals along each transect for comparison. Data were compared between the two on-the-go sensors and were also related to penetrometer and soil property data. The repeatability of SCPS data was somewhat better than that of SSPS data. Data from the two sensors were linearly related, with similar regression equations for each individual site and for both sites combined. The agreement between SCPS and SSPS data (r2 = 0.56 over all sites and depths) was much better than between sensor and penetrometer data (r2 = 0.19-0.20). Maps of SCPS and SSPS data for a 13.5-ha field site showed very similar patterns. Maps of penetrometer data were also similar to those of on-the-go sensor data, but showed fewer spatial details. Variation in soil strength appeared to be primarily related to variations in soil physical properties (e.g., texture, water content). Due to the similarity between SCPS and SSPS data, we conclude that measurements obtained with the two on-the-go soil sensors were affected similarly by soil strength variations within the study sites. Side-by-side comparison of the on-the-go sensors provided a convenient approach to validate sensor performance. The study also provided information to improve on-the-go sensor design and to relate sensor data to other measures of soil compaction. © 2007 Elsevier B.V. All rights reserved.
• 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.
• Andrade-Sánchez, P., Upadhyaya, S. K., & Jenkins, B. M. (2007). Development, construction, and field evaluation of a soil compaction profile sensor. Transactions of the ASABE, 50(Issue 3).
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  Studies have shown that an increased level of soil compaction leads to a reduction in infiltration characteristics of soil, which in turn leads to low soil moisture. Conventional methods of measuring soil compaction are tedious, time consuming, and expensive. The objective of this study was to develop and evaluate a soil compaction profile sensor (SCPS) that could assist in the assessment of the state of compactness of the soil profile in real-time. The device developed in this study consisted of eight cutting elements, designed to provide information on soil resistance to cutting for every 7.5 cm layer down to a total depth of 60 cm. The design produced a sensor with a backward-sloping rake angle and a total thickness of 5.1 cm. Extensive field tests were conducted during summer and fall of 2001 and spring of 2002 in loamy, clayey, and sandy fields. Within each soil type, three different moisture conditions were included in the test (low, medium, and high). Analysis of the test data revealed that the soil cutting force was a function of soil bulk density, moisture content, and the location of the cutting element within the soil profile. Additional analyses were conducted to relate soil cutting force profile to the cone index profile. The empirical relationship between predicted and measured profile sensor output had a coefficient of multiple determination (R 2) of 0.977, indicating that the SCPS can potentially be used to make real-time measurements of soil strength profile. © 2007 American Society of Agricultural and Biological Engineers.
• 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 American Society of Agricultural Engineers, 48(Issue 6).
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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. © 2005 American Society of Agricultural Engineers.
• 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-Sánchez, P., Upadhyaya, S. K., Agüera-Vega, J., & Jenkins, B. M. (2004). Evaluation of a capacitance-based soil moisture sensor for real-time applications. Transactions of the American Society of Agricultural Engineers, 47(Issue 4).
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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., 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., Heun, J. T., Gore, M. A., French, A. N., Carmo-Silva, E., & Salvucci, M. E. (2012). Use of a moving platform for field deployment of plant sensors. In American Society of Agricultural and Biological Engineers Annual International Meeting 2012, 4.
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  The development of new soil and plant sensors continues to be an area of emphasis in precision agriculture. Sensing technology can be used in plant breeding to facilitate selection of plant varieties with superior traits and in management to optimize production inputs for crop protection and growth. One example is the use of spectral sensors for site-specific fertility management applications when they are integrated with GPS and rate controllers. In spite of the progress made in sensor development, there is still a need to investigate methods for field deployment of these sensors with the use of ground systems. In this paper, we analyze the benefits and limitations of a ground-based proximal sensing platform retrofitted with plant height, canopy temperature and reflectance sensors. The platform consisted of a high-clearance sprayer modified with a front boom for sensor mounting. Other instrumentation included a GPS-RTK for positioning data and sensor data acquisition system. Machine field capacity, timeliness and other factors are analyzed in the context of field operation, and a general description of the vehicle operational characteristics are given from the mechanical perspective.
• 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.
• Subramani, J., Martin, E. C., & Andrade-Sanchez, P. (2010). Assessing irrigation BMP for water conservation - Land leveling. In 2010 ASABE Annual International Meeting.
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  In 2001, the Arizona Department of Water Resources implemented an agricultural Best Management Practice (BMP program). The program was designed to encourage the use of BMP in irrigation with goal of increasing efficient use of water resources on the farm. Several BMP were identified through stakeholder meetings and meeting with researchers and scientists. One of the BMPs identified was annual laser touchup to maintain a level field slope. This study was designed to determine the impact of yearly laser leveling on irrigation water applications and yield. There were two treatments, no laser leveling and laser leveled. There were no significant differences due to leveling for yield in any of the three years. Lint yield for no laser leveling and laser leveled treatments was 2010 and 1990 kg/ha (1795 and 1777 lb/ac) in 2006, 1780 and 1829 kg/ha (1589 and 1633 lb/ac) in 2007, and 1564 and 1687 kg/ha (1397 and 1507 lb/ac) in 2008 respectively. The amount of water irrigated was 167.6 cm (66 inches) for no laser leveled vs. 154.9 cm (61 inches) for laser leveled in 2007, and 226.1 and 154.9 cm (89 and 61 inches) in 2008.
• 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.
• Andrade-Sanchez, P., Pierce, F. J., & Elliott, T. V. (2007). Performance assessment of wireless sensor networks in agricultural settings. In 2007 ASABE Annual International Meeting, Technical Papers, 7.
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  Wireless Sensor Network (WSN) utilizes radios operating primarily in the 900 MHz and 2.4 GHz frequency bands. In general, as frequency increases, bandwidth increases allowing for higher data rates but power requirements are also higher and transmission distance is considerably shorter. In general, depending on the operating environment, significant signal loss can occur at these frequencies particularly when the radios require line-of-sight for optimal performance, with 2.4 GHz more susceptible than 900 MHz. For agricultural applications, WSN must be able to operate in a range of environments, from bare fields to orchards, from flat to complex topography, and over a range of weather conditions, all of which affect radio performance. However, there are limited data on radio performance as affected by agricultural setting and no standard tests are available for quantifying WSN performance in agricultural applications. Using a low powered, 10 mW 900 MHz frequency hopping spread spectrum radio, we developed a range of tests intended to quantify the performance of agricultural WSN in fields, vineyards, and orchards over a range of crop and weather conditions. Performance data include different metrics of radio performance such as packet delivery and signal strength along with power consumption tests under different supply strategies. This paper evaluates the extent to which various tests can be used to quantify WSN performance and how WSN perform under various cropping systems. Extensive field tests are in progress at Washington State University, with the objective of generating a greater understanding of environmental variables affecting the performance of WSN in outdoor conditions. The field deployment uses a radio system of 900 MHz Spread Spectrum technology based on a low-power, radio transceiver (Chipcon-CC1100) and applied to a variety of spatial configurations. Performance evaluation tests include different metrics of radio performance such as packet delivery and signal strength. Moreover power consumption is tested under different supply strategies.
• Andrade, P., Upadhyaya, S. K., Jenkins, B. M., Plouffe, C., & Poutre, B. (2004). Field evaluation of the improved version of the UCDavis compaction profile sensor (UCD-CPS). In ASAE Annual International Meeting 2004.
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  Our experience with the development of the first prototype of the UC Davis soil compaction profile sensor allowed us to define design parameters for improvements in three areas: cost, size, and operational characteristics. The goal was to design, fabricate, and test a field-ready, cost-effective device with enhanced capabilities to sense the differences in soil compaction along a profile up to a depth of 46 cm. The new design has close resemblance to commercially available subsoiler shanks. This new sensor used five custom-made load cells with their rated capacity adjusted to their location along the shank to achieve consistent sensitivity. Cutting elements of 63.5 mm in height were directly connected to these force transducers. The total thickness of the sensor was 28.6 mm. Field tests were performed on a Yolo loam soil at different moisture contents. The results of this phase indicated that the new sensor had similar response characteristics as the older prototype. The output of this sensor correlated with soil moisture content and density just like in the case of its predecessor. Moreover, its output also correlated with soil Cone Index values very well. This sensor was interfaced with a Differential Global Position System (DGPS) to geo-reference its output. Field evaluation was performed at the farm level in a variety of soils typical of the US Mid-west. Numerous Cone Penetrometer readings were obtained to compare with the output of the soil compaction sensor. Except in stony soils, the sensor was able to stand the field conditions during extensive fieldwork with significant reliability. Results indicate that the improved UCD-CPS can detect differences in the compaction state of the soil profile reasonably well.
• Andrade, P., Mitchell, J., Jenkins, B., & Upadhyaya, S. (2000). Principal components approach for the linearization of yield prediction and tilth index equations. In 2000 ASAE Annual International Meeting, Technical Papers: Engineering Solutions for a New Century, 2.
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  After 11 consecutive years of sustained farm input management treatments, a selection of soil physical properties was analyzed using a multivariate scheme to explore the impact that management systems (conventional, low input, and organic) have had on the physical state of Yolo loam soil. The crop in question was dry beans, and the time frame was the beginning of the flowering stage (46 days after planting). Principal Component Analysis was used to reduce the number of variables required to estimate the Tilth Index. The above strategy resulted in the suppression of multicollinearity, which in turn allowed for a more reliable estimation of variable coefficients once they were transformed back to the original coordinate system. Based on empirical considerations in recent reports of soil tilth definition and quantification, alternative linear expressions of the soil tilth index were developed using the coefficients of a principal component analysis. Even though this brought apparent improvements in the alternative expressions of soil tilth index, such as values having a wider range while remaining normalized from zero to unity, correlation with yield did not improve significantly.
• Andrade, P., Mitchell, J., Jenkins, B., & Upadhyaya, S. (2000). Soil strength dynamics under different management systems. In 2000 ASAE Annual International Meeting, Technical Papers: Engineering Solutions for a New Century, 2.
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  The dynamic behavior of Yolo loam soil in terms of changes in its mechanical strength was analyzed for three management systems (conventional, low input, and organic) which have been established as experimental plots for the last 11 years. The above analysis was conducted for dry beans during the summer months of 1999 and required three determinations at different vegetative stages (post-emergence, flowering, and harvest). Measurements consisted of weekly monitoring of soil moisture content as well as three determinations of Soil Cone Index (CI), and the tractive force required to pull a vertical, 30-cm-deep chisel (TCI) through the soil. All the measurements were done on both trafficked and non-trafficked furrows, and they consisted of nine sampling points for the CI per plot and continous measurement of TCI by means of GPS-aided instrumentation. The results for an integrated layer of 0-30 cm depth show that the most significant differences occurred at the time of highest demand of water from the crop. As time passed, soil strength decreased to values close to the initial ones without significant differentiation among management systems. Closer inspection of the data suggested that integrated values for the 0-30 cm. stratum at the beginning and at the end of the cropping cycle seemed to be quite similar, but there were important variations in soil hardness in sub-layers within this stratum. Throughout the season, management systems were clearly differentiated in regard to the water-holding capacity of the soil; this was reflected in the inverse relationship between the applied chemical inputs and the moisture content.
• 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.
• Diaz, D., Ottman, M. J., 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.
• 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.

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