4. Deriving plant parameters for a forage species and accommodating ecotypes

#### 4.1. Field plant species measurements

the current growth stage, the demands for N and P are calculated. If insufficient N and/or P is present in the current rooting zone, the model reduces simulate growth rates to account for N and/or P stress. This simulation of the balance of nutrients is done for each species within a mixture. The model accounts for variability in root scavenging capacities between species only through differences in the current rooting depth of each species. Potential rooting depths of various plant species are derived from measurements reported in the literature for forages

Likewise, potential plant transpiration is calculated from the potential evapotranspiration and the total community LAI. If soil water in the current rooting zone is insufficient to meet the species' demand, simulated drought stress occurs and limits growth. This occurs for all plant species present. However, it should be noted that a deeper rooted plant species may have access to soil water (and nutrients) not available to any competing shallower rooted species; ALMANAC accommodates different rooting depths of species. The deeper rooted plant species may have adequate soil water and nutrients to avoid drought and nutrient stresses when a shallower rooted species is stressed. The ALMANAC model does not currently simulate hydraulic lift dynamics and the potential impacts of lift on water and nutrient redistribution.

The soils and weather data described below are specific to the U.S. These are described in more detail, including how to download them at: https://www.ars.usda.gov/plains-area/temple-tx/ grassland-soil-and-water-research-laboratory/docs/193226/. The soil and weather data for the country of Mexico have also been developed and formatted for ALMANAC model simulations (https://www.ars.usda.gov/plains-area/temple-tx/grassland-soil-and-water-research-laboratory/ docs/almanacmex/). As the model is applied outside of these two countries, the input data for soils and weather (as well as plant species growth curves) can be developed through coopera-

The general philosophy of input data development is to make this model and other USDA-ARS models (including EPIC [25], APEX [26, 27], and SWAT [28–30]) readily and easily applied. Input data are constrained by what is readily available and easily accessible. This means the daily weather inputs required consist of maximum and minimum temperature, rainfall amounts (and snowfall amounts), and solar radiation. When unavailable for a given location, solar radiation can be derived; wind speed and relative humidity can be used to approximate solar radiation. Weather data from the U.S. National Oceanic and Atmospheric Administration (NOAA) websites are readily downloaded for any state in the U.S. via the

Similarly, required soil data are available through USDA-NRCS (https://www.nrcs.usda.gov/ wps/portal/nrcs/main/soils/survey/), which has the most extensive and verified, publiclyavailable soil database for the U.S. The soil data are readily downloaded for any state in the U.S., with the steps outlined in the ALMANAC model documentation. The most critical components of the soil data inputs are the depth, texture, and amount of rocks by soil layer.

steps outlined in the model documentation (see website link above).

grown on soils with no restrictive soil layers (such as in [17]).

3. Soils and weather data

44 Forage Groups

tion with the senior author of this project.

The group of readily derived plant parameters includes the potential leaf area index (LAI), the development curve for LAI over the growing season, the light extinction coefficient for Beer's law (k), the radiation use efficiency (RUE), the duration of the season in degree days, the harvest index for seeds (HI), and the N and P concentrations for each species over the growing season. All of these should be derived from measurements of a plant stand grown in a relatively stress-free environment to establish potential values for these for each forage species and ecotype. This means that ideally species being measured in field conditions should not have stresses due to drought or nutrient deficiency.

Details on taking field measurements for deriving plant parameters are outlined in detail under the headings: "Gathering Field Data, How to Use Ceptometer: AccuPAR LP-80 Basics Standard "[31] and "Taking measurements for ALMANAC: Sampling Protocol Standard with Photos" (https://www.ars.usda.gov/plains-area/temple-tx/grassland-soil-and-water-research-laboratory/docs/193226/) (Figure 3).

Field-derived values for the critical species-specific parameters have been described previously [17–21]. The model simulates light interception by the leaf canopy with Beer's law [16]

Figure 3. Intercepted photosynthetically active radiation (IPAR) measurements using an AccuPAR LP-80 Ceptometer at Bishop, California, and Bryan, Texas.

and the LAI. Larger values of the extinction coefficient have more light intercepted at a given LAI.

b. Use a belt-driven leaf area meter (or something similar) to measure leaf area of the subsample. Separate the subsample into dead material (anything completely brown), stems, leaves, and reproductive structures. Record the weight of the dead material, stems,

c. Determine the area of each structure using the leaf area meter. Run the dead material, stems, leaves, and reproductive structures through separately and record the area of each.

for 3 days or until weight stabilizes. Then record the dry weight of the entire sample.

Following successful calibration of the ALMANAC model with field measured parameters, the model was applied to simulate forage yields across years and diverse environments in the U.S. For model testing, we used published forage yield data from Natural Resources Conservation Service, United States Dept. of Agric. 2017. Web Soil Survey. Available online: http://websoilsurvey.sc.egov.

Many common native and introduced grasses or grass mixtures in the U.S. have annual productivity values reported as USDA-NRCS ecological site productivity (for native forages) or NRCS crop productivity (for improved grasses) for many representative areas. As discussed below, once plant parameters for a particular forage are derived, they are tested on different soils in contrasting U.S. counties. The counties simulated are selected because they have soils with quantified annual biomass yields for the forage of interest (NRCS Web Soil Survey)

Total annual production of forages reported by NRCS are derived from end-of-season sampling on sites with closed canopy stands of the species of interest over 3 years or more. The NRCS procedure involves measuring dry matter biomass production above a 5-cm cutting height in at least 10 randomly selected plots at each field site. The specific soils for a location of interest can be downloaded as described above. Mean simulated forage yield over 10 years of real weather data can be compared to the reported annual production (from USDA-NRCS Web Soil Survey) for a site. The NRCS value of Animal Unit Month (AUM) is converted to Mg ha<sup>1</sup> (0% moisture) with a conversion factor assuming 700 lbs. (318 kg) of air-dried biomass (90% moisture) per AUM. Values for key plant parameters for the plant species of interest are

We have several published examples of testing ALMANAC's simulation of forage yields. The first was for several Texas range sites with native warm-season grasses [32, 33]. Next, we simulated old world bluestems (Bothriochloa Kuntze, Capillipedium Stapf, and Dichanthium

C forced air oven

47

Forage Yield Estimation with a Process-Based Simulation Model

http://dx.doi.org/10.5772/intechopen.79987

Place the entire sample into a paper bag. These samples are dried in a 66

d. Finally, grind dry sample to prepare for nutrient analysis.

5. Model testing against independent data

(http://websoilsurvey.nrcs.usda.gov/app/HomePage.htm).

derived from the field measurements described above.

5.1. Examples of testing ALMANAC's simulation of forage yields

and reproductive structures.

usda.gov/App/WebSoilSurvey.aspx.

Measurement of light interception by the plant canopy is described at the website: https:// www.ars.usda.gov/plains-area/temple-tx/grassland-soil-and-water-research-laboratory/docs/ 193226/. To derive leaf area, biomass, and the extinction coefficient for Beer's law, LAI measurements are derived every 2 weeks during the active growing season via light measurements taken above and below the canopy between 10 a.m. and 2 p.m. on a clear day. The Decagon ceptometer (or something similar) is used to measure light as photosynthetically active radiation, since those are the wavelengths critical for photosynthesis. A random sample area for the area of interest is chosen where the forage is growing. The stand in the area for taking light measurements should not be trampled. Areas adjacent to where previous samples were taken should be avoided and should be ungrazed. A quadrat 0.5 m wide by the length of our light bar (0.8 m) is reasonable for the sampling area.

If there are any non-targeted plants in or overshadowing our quadrat, they should be removed, or the quadrat should be relocated. Only canopy cover from targeted specie should be measured. The time of day, average phenology, and the average plant height in centimeters should be recorded. Light interception readings using the ceptometer are taken as:


Repeat these steps at least three more times for a targeted plant species. For each set of measurements, make sure to measure plants on the same soil or ecological site. When returning to the general area for future measurements, select the same species to measure but not the exact same plant/plot area as previously measured.

Process plant material as soon as possible after sampling to avoid desiccation effects on leaf area.

a. When weighing the entire sample from field, if the entire sample is greater than 100 g, take a representative subsample. This is between 10 and 30% of the entire sample but no less than 100 g. Weigh and record the subsample weight. Make sure to select a subsample with the same proportion of green leaves, dead material, stems, and reproductive structure as the entire sample.

