Progress 09/01/17 to 08/31/20
Outputs
Target Audience:The target audience for our project were alfalfa growers, seed dealers, extension specialists, researchers, and feeders. Findings from our project were presented at several international and national conferences as well as local extension meetings and field days. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?One postdoc research associate was trained as part of this project. The postdoc was trained in crop modeling and decision support development. One PhD student also recieved crop modeling training as part of this grant. How have the results been disseminated to communities of interest?Findings were disseminated through extension and professional meetings as listed below: No. Conference/ meeting Title of presentation Audience 1 AgMIP Global workshop, April 24th, 2018 San Jose CA Introducing the CROPGRO Perennial Forage Model for Tropical and Temperate Grasses and Legumes Researchers, extension specialists, policymakers 2 Montana State University Northwestern Agricultural Research Center Field, Jul 10, 2018 Growing alfalfa on different soil moisture availability Growers, dealers, policy markers 3 Alfalfa and Forage "Tent Talks" Jul 11, 2018, Jason Rovey's Farm, Buckeye, AZ 85326 The Costs Associated with Alfalfa Cutting Frequency Growers, dealers, policy markers 4 USCID 11th International Conference on Irrigation and Drainage, Oct.16-19, 2018, Phoenix, AZ Managing irrigation with limited water using iCrop: Case Study of Alfalfa Researchers, extension specialists, policymakers, private sector representatives 5 International Forage & Turfgrass Breeding Conference, Mar 24th, 2019 Lake Buena Vista FL Evaluating Cultivar and Species Traits with the CROPGRO Perennial Forage Model for Grasses and Legumes Growers, alfalfa seed dealers, researchers, extension personnel, landscapers, policymakers, feeders. 6 2020 Agriculture Faculty Academy Webinar Series Adaptive Irrigation Management Using the Food, Agriculture & Resource Management System (FARMs) Researchers, extension personnel, instructors, private industry 7 International Crop Modeling Symposium 2020, Feb 3-5, 2020, Montpellier, France. Improving the CROPGRO Perennial Forage Model for Ability to Simulate Fall Dormancy Classes of Alfalfa Cultivars Researchers, policymakers, private sector What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
Objective 1: Accomplishments Our goal was to adapt the CROPGRO model in DSSAT to stimulate growth, yield, and quality of alfalfa. To accomplish this objective required adapting or developing new algorithms to handle growth characteristics unique to alfalfa. The following code changes were implemented in order to simulate alfalfa forage yield and quality. Added the ability to re-grow based on reserves despite zero LAI. Created memory of poor prior management (low reserves). Created memory and mechanism for winter dormancy. Added new state variable (stolon-rhizome-storage tissue) with TNC and N concentration Added rules for partitioning DM, N, and TNC to storage tissue as a function of day length, photosynthesis, and LAI Added rules for mobilization of C and N reserves from storage for re-growth as a function of day length, photosynthesis, and LAI Fall dormancy classes were established, along with parameters to mimic differential dormancy, reserve allocation, and regrowth. In addition to the above code changes, a new input file called the "MOW" file was created to handle harvest schedules. In adapting the CROPGRO Perennial Forage Model (CROPGRO-PFM), two approaches were followed: 1) adopting biophysical relationships reported in the literature and 2) comparison to experimental data. Detailed field experiments were conducted on different fall dormancy (FD) cultivars under two contrasting climates in Montana and Arizona. Sample comparison between simulated and observed alfalfa growth and yield are shown in Fig. 1 to 4. The fall dormancy effect was created via daylength effects on partitioning to storage reserves in taproot, as well as daylength effects on strength of mobilization from storage reserves to drive re-growth. The critical daylengths for partitioning to reserves in taproot were set at 9.8 h (for maximum rate to storage) and 14.2 h (for minimum rate of allocation to storage). This means that under short days, more assimilate goes to taproot reserves and less to shoot growth. The model has a baseline partitioning to storage along with effects of LAI to refill, but the daylength function modifies the partitioning, and the strength of FD class is set by RDRMT (value varies by FD class, from 0.140 for CUF101 with FD9 to 0.500 for Rugged FD3). The fall dormancy effect also operates via critical daylength effects on the mobilization rate from storage which has critical daylengths for minimum mobilization at 9.9 h, with maximum mobilization rate at 13.9 h, with the strength set by RDRMM which varies slightly by FD class. Just like the partitioning function, the mobilization rate has a baseline rate that is strongest at low LAI and is reduced as LAI increases. The two functions, allocation to storage and mobilization run continuously during simulations but are sensitive to the daylength, LAI, and photosynthetic rates. Based on the growth analysis data, we found it necessary to modify genetic potential light-saturated photosynthetic rate by FD class, with an increase in LFMAX by approximately 0.02 mg m-2 s-1 per FD, ranging from 1.32 mg m-2 s-1 for Rugged (FD3) to 1.46 mg m-2 s-1 for CUF101 (FD9). Apparently, tolerance to freeze and winter survival appears to have a cost in terms of photosynthetic ultra-structure or enzymatic function. The success in creating the fall dormancy effect was, in large part, attributed to the differential growth responses of different FD class cultivars (Rugged, FD3; Cisco II, FD6; and CUF101, FD9) grown and measured in Arizona and Montana. The development of the FD functions was assisted by and made consistent with data from Onedia (FD 3) and Apica (FD 4) cultivars in Canada (Jing et al., 2019), as well as data on Aragon (FD7) grown in Spain. Table 1 below, lists the FD class of cultivars along with the corresponding parameters for strength of allocation to storage (RDRMT), strength of mobilization (RDRMM), and LFMAX (light-saturated photosynthesis). Differences not yet attempted include the fact that rate of leaf node appearance and height also varied among FD classes (being faster and taller for high FD), and that CUF101 had an earlier onset of leaf appearance in the spring. In addition, the leaf to stem ratio appeared to be less for the high FD classes, this is also not modeled yet. Table 1 available at https://www.dropbox.com/s/2p7q1zncy9z5nt6/Final_Alfalfa_Project_Report_11282020.pdf?dl=0 Figure 1 illustrates how the CROPGRO-PFM-Alfalfa model simulates LAI for three FD class cultivars in Arizona. Observed LAI was greater for CUF101 (FD9) than Cisco II (FD6) than Rugged (FD3), especially during the short-days of fall, and the model was able to capture that response with the modifications of the strength of daylength effect on dormancy and mobilization of reserves for regrowth, along with small differences in photosynthetic rate. Figure 2 illustrates the biomass growth dynamics over time, which like the response of LAI, shows that the model modifications succeeded in capturing the greater biomass accumulation of CUF101 (FD9) compared to Cisco II (FD 6) and compared to Rugged (FD 3). Figure 3 shows the model-simulated dynamics of carbohydrate reserves in taproot, rate of re-fill, rate of mobilization for regrowth, and leaf area growth of CUF101 cultivar during seven growth cycles. While there were no measurements of taproot mass or carbohydrates, the pattern does mimic limited published literature on alfalfa. Figure 4 shows biomass growth dynamics overtime for two cultivars, Rugged FD3 and Cisco II (FD6) grown over two harvest cycles at the Montana State University Northwestern Agricultural Research Center. Figures 1, 2, 3 & 4 available at https://www.dropbox.com/s/2p7q1zncy9z5nt6/Final_Alfalfa_Project_Report_11282020.pdf?dl=0. ?Objective 2: Accomplishments Field experiments were successfully established at the Montana State University Northwestern Agricultural Research Center and the University of Arizona Maricopa Agricultural Center that produced useful data that was used in developing the alfalfa model. Full details available at https://www.dropbox.com/s/2p7q1zncy9z5nt6/Final_Alfalfa_Project_Report_11282020.pdf?dl=0 Objective 3: Accomplishments FARMs web application was developed. A major advantage of FARMs is that it can produce in-season yield predictions which are important for adaptive management e.g., evaluating the effect of irrigation on cutting schedule. In FARMs, the portion of the growing season simulated using historical climatic data decreases as simulation time approaches the end of the season. Detailed descriptions of the FARMs web application can be found in Kim and Kisekka, (2020). The FARMs web app can be accessed at https://ciswma.lawr.ucdavis.edu/FARMS-BETA/farms. The following specific tasks were accomplished: DSSAT-CSM source code with CROPGRO-PFM-Alfalfa model was obtained from the GitHub DSSAT repository. Compiled the DSSAT source code in Ubuntu/Linux in order for it to be executed from the server. The front-end user interface of FARMs was improved to reflect changes in inputs and outputs that are unique to the new alfalfa module including yield from multiple cuttings, forage quality. Tested the performance of the new alfalfa module in FARMs using alfalfa variety trial data from the University of California Cooperative Extension . Made changes in the FARMs based on user feedback to reflect alfalfa irrigation management practices in California, Arizona, and Montana. Developed user tutorials for FARMs with specific instructions for setting up a field and alfalfa scenarios, the tutorials are available at http://kisekka.ucdavis.edu/software/farms/ More details available at https://www.dropbox.com/s/2p7q1zncy9z5nt6/Final_Alfalfa_Project_Report_11282020.pdf?dl=0
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
**Hi Isaya: please refer to the attached project initiation proposal from 2017 information to help update in this final report**
notes/details about Publications:
Wafa Malik, Kenneth J. Boote, Gerrit Hoogenboom, Jos� Cavero, Farida Dechmi. 2018. Adapting the CROPGRO Model to Simulate Alfalfa Growth and Yield. Agronomy Journal. 110 (5):17771790.
Publication Definitions:
Publications are the characteristic product of research. Agencies evaluate what the publications demonstrate about the excellence and significance of the research and the efficacy with which the results are being communicated to colleagues, potential users, and the public, not the number of publications.
Journal publications: Peer-reviewed articles or papers appearing in scientific,
technical, or professional journals. Include any peer-reviewed publication in the
periodically published proceedings of a scientific society, a conference, or the like. A
publication in the proceedings of a one-time conference, not part of a series, should be
reported under Books or other non-periodical, one-time publications.
Books or other non-periodical, one-time publications: Any book, monograph,
dissertation, abstract, or the like published as or in a separate publication, rather than a
periodical or series. Include any significant publication in the proceedings of a one-time
conference or in the report of a one-time study, commission, or the like.
Other publications, conference papers and presentations: Identify any other
publications, conference papers and/or presentations not reported above.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Jing, Q., B. Qian, G. B�langer, A. VanderZaag, G. J�go, W. Smith, B. Grant, J. Shang, J. Liu, W. He, K. Boote, and G. Hoogenboom. 2019. Simulating alfalfa regrowth and biomass in eastern Canada using the CSM-CROPGRO-Perennial forage model. Eur. J. Agron. 113. https://doi.org10.1016/.eja2019.125971
- Type:
Journal Articles
Status:
Submitted
Year Published:
2020
Citation:
Kim, J., and I. Kisekka. FARMs: A web-based geospatial crop modeling and agricultural water management application. Environmental Modeling and Software. ** SUBMITTED **.
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