Integration of Unmanned Aerial System (UAS) Data and Process Based Simulation Models to Forecast Crop Growth and Yield

Transcription

1 Integration of Unmanned Aerial System (UAS) Data and Process Based Simulation Models to Forecast Crop Growth and Yield Juan Landivar 1, Jinha Jung 2, Long Huynh 2, Murilo Maeda 1 and Andrea Maeda 1 (1)Texas A&M AgriLife Research, Corpus Christi, TX, (2)Texas A&M University Corpus Christi, Corpus Christi, TX,

2 Objectives Develop soils, weather and agronomic input database to calibrate and evaluate the cotton simulation model GOSSYM Develop an user interface to link GOSSYM with a precision agriculture database (grid) Use GOSSYM to forecast growth and yield of cotton crops

3 GOSSYM (Gossypium simulation) Background GOSSYM is a cotton simulation model which simulates the main processes affecting growth and yield in the soil plant atmosphere system. GOSSYM is a materials balance model which provides daily simulation based on soil, cultural practices and weather input information. Key References: Baker, D. N., J. R. Lambert, and J. M. McKinion GOSSYM: A simulator of cotton crop growth and yield. South Carolina Agr. Expt. Sta. Tech. Bull Whisler, F. D., B. Acock, D. N. Baker, R. E. Fye, H. F. Hodges, J. R. Lambert, H. E. Lemmon, J. M. Mckinion, and V. R. Reddy Crop simulation models in agronomic systems. Adv. Agron. 40: Lemmon. H COMAX: An expert system for cotton crop management. Science 233:29 33 Landivar, J.A., K.R. Reddy and H.F. Hodges Physiological Simulation of Cotton growth and Yield. In Steward J.McD et al. (eds), Physiology of cotton. Chapter 28. pp DOI / _28. Springer Science.

4 User interface Simulation of soil water potential and Volumetric water content

5 Simulation of soil Nitrate and Ammonia content

6 Simulated Stages of Development First Square First Bloom/boll First open boll Full Maturity

7 UAS Platform Data Extraction Scheme Temporal plant growth patterns Plant height, cm Canopy cover, % Biomass, Kgs / m Plant health (temporal) Reflectance, NDVI Canopy Temperature, IR Maturity and yield parameters Bloom count, Maturity Open Boll Count, yield Growth Analysis Sigmoidal growth curves, growth rates Simulation GOSSYM, a process level, physiological model 2016 Study 30 Cultivars 2 Water regimes 4 Replications 2 Rows per entry 10 Grids per Row 4800 Measurements per flight Use all of the above to evaluate and rank genotypes based on specified goals (earliness, drought tolerance, diseases, nematodes, yield)

8 20 Blocks (1 m 2 ) /Entry (4800 grids) (Plant height, canopy cover, canopy Volume, ExG, NDVI, canopy temperature, bloom, open boll count)

9 Plant Height, m

10 Canopy Cover, %

11 Remote Sensing and GOSSYM Simulations Cultivar and Agronomics Cultivar Characteristics Planting date Irrigations Fertilizers Growth Regulators Energy Balance Soil Evaporation Plant Transpiration Water Stress Index Irrigation Needs Photosynthesis Leaf Weight Boll Weight Stem Weight Root Weight Phenology Main stem nodes Flower buds Green bolls Open bolls Maturity and Yield Cutout Defoliation Time Yield Fiber quality

12 Calibration of GOSSYM PHY 499, DryLand, 2016 Corpus Christi, TX.

13 UAS Measured Canopy Cover vs GOSSYM Simulation PHY 499

14 GOSSYM Simulations with UAS Canopy Cover as input PHY 499, DryLand, 2016 Corpus Christi, Tx

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16 Simulated Lint Yield, Lb/A 5241 PHY499 CA CULTIVAR

17 Conclusions GOSSYM was successfully calibrate to simulate plant growth parameters and yield for cultivar PHY 499 A dynamic users interface was developed which links GOSSYM with our UAS generated database, facilitating the creation and edit of input files, providing graphic display of plant phenology, soil water and nitrogen profiles. GOSSYM was used to explore the response of cotton cultivars to various patterns of canopy development as measured with our UAS platform. The simulations provided food for thought to the interaction between canopy characteristics with water and nitrogen use. We plan to use physiological models such as GOSSYM as analytical tools to enhance our understanding of how plant parameters measured with our UASplatform influence the performance of a genotype under various environmental scenarios