Agricultural Land Management Alternatives With Numerical Assessment Criteria Research Articles

Modeling Climate Warming Impacts on Grain and Forage Sorghum Yields in Argentina

Sorghum is the world’s fifth major cereal in terms of production and acreage. It is expected that its growth will be affected by the increase in air temperature, an important component of global climate change. Our objective was to use the Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model to (a) evaluate the impact of climate warming on forage and grain sorghum production in Argentina and (b) to analyze to what extent yield changes were associated with changes in water or nitrogen stress days. For model calibration, we used previous information related to the morpho-physiological characteristics of both sorghum types and several soil parameters. We then used multiyear field data of sorghum yields for model validation. Yield simulations were conducted under three possible climate change scenarios: 1, 2, and 4 °C increase in mean annual temperature. ALMANAC successfully simulated mean yields of forage and grain sorghum: root mean square error (RMSE): 2.6 and 1.0 Mg ha−1, respectively. Forage yield increased 0.53 Mg ha−1, and grain yield decreased 0.27 Mg ha−1 for each degree of increase in mean annual temperature. Yields of forage sorghum tended to be negatively associated with nitrogen stress (r = −0.94), while grain sorghum yield was negatively associated with water stress (r = −0.99). The information generated allows anticipating future changes in crop management and genetic improvement programs in order to reduce the yield vulnerability.

Field and simulation‐based assessment of vetivergrass bioenergy feedstock production potential in Texas

AbstractVetivergrass [Chrysopogon zizanioides (L.) Roberty] is a multi‐purpose crop that has an untapped potential for biofuel production. We conducted a field study at Temple, TX, to determine plant growth characteristics that make vetivergrass an ideal candidate bioenergy feedstock crop. Overall, the high biomass yield (avg. 18.4 ± 0.7 Mg ha−1) can be attributed to the high leaf area index (LAI, avg. 12.7 ± 2.5) and crop growth rates that ranged from 2.7 ± 0.1 to 15.7 ± 0.1 g m−2 d−1. Plant tissue N and P concentrations ranged from 0.59–1.66% and 0.06–0.15%, respectively. Surprisingly, the radiation use efficiency (RUE, avg. 2.2 ± 0.1 g MJ−1) was not high relative to other highly productive grasses. Biomass yield was highly correlated to plant height (avg. 2.1 ± 0.1 m) and LAI (Pearson, r = 0.96 and 0.77, respectively). Data from the field experiment provided plant coefficients that were used to develop an Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) vetivergrass model to assess dryland and irrigated interannual and spatial biomass yields across Texas. ALMANAC simulated dryland and irrigated yields ranged from 0.8–39.3 (avg. 17.2) Mg ha−1 and 9.1–47.0 (avg. 25.4) Mg ha−1, respectively. There was huge spatial variation in dryland and irrigated yields, with CV values of 20 and 15%, respectively. Similarly, dryland and irrigated inter‐annual yields respectively had CV values of 25 and 17%. State‐wide simulation model assessments complement field studies, and furthermore allow bioenergy companies and investors to better estimate biofuel feedstock potential for new crops such as vetivergrass.

Potential impact of future climate change on sugarcane under dryland conditions in Mexico

AbstractAssessments of impacts of future climate change on widely grown sugarcane varieties can guide decision‐making and help ensure the economic stability of numerous rural households. This study assessed the potential impact of future climatic change on sugarcane grown under dryland conditions in Mexico and identified key climate factors influencing yield. The Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model was used to simulate sugarcane growth and yield under current and future climate conditions. Management, soil and climate data from farm sites in Jalisco (Pacific Mexico) and San Luis Potosi (Northeastern Mexico) were used to simulate baseline yields. Baseline climate was developed with 30‐year historical data from weather stations close to the sites. Future climate for three decadal periods (2021–2050) was constructed by adding forecasted climate values from downscaled outputs of global circulation models to baseline values. Climate change impacts were assessed by comparing baseline yields with those in future decades under the A2 scenario. Results indicate positive impacts of future climate change on sugarcane yields in the two regions, with increases of 1%–13% (0.6–8.0 Mg/ha). As seen in the multiple correlation analysis, evapotranspiration explains 77% of the future sugarcane yield in the Pacific Region, while evapotranspiration and number of water and temperature stress days account for 97% of the future yield in the Northeastern Region. The midsummer drought (canicula) in the Pacific Region is expected to be more intense and will reduce above‐ground biomass by 5%–13% (0.5–1.7 Mg/ha) in July–August. Harvest may be advanced by 1–2 months in the two regions to achieve increases in yield and avoid early flowering that could cause sucrose loss of 0.49 Mg ha−1 month−1. Integrating the simulation of pest and diseases under climate change in crop modelling may help fine‐tune yield forecasting.

A Large Scale GIS Geodatabase of Soil Parameters Supporting the Modeling of Conservation Practice Alternatives in the United States

Water quality modeling requires across-scale support of combined digital soil elements and simulation parameters. This paper presents the unprecedented development of a large spatial scale (1:250,000) ArcGIS geodatabase coverage designed as a functional repository of soil-parameters for modeling and comparison of water quality outcomes in the United States. The set of target models include: SWAT (Soil and Water Assessment Tool), APEX (Agricultural Policy Environmental Extender), and ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria). This development relies on the Digital General Soil Map (DGSM) as the source of soil information, and leverages on architectural design and associated tools created for a companion product at higher resolution from which also was extended a procedure for refilling a large number of missing derived parameters. Outlined by regional watershed layouts and supported by GIS land use layers, the core product is developed using the File Geodatabase (FGDB) data structure, which brings, via customized Python-based tools, the data directly into geoprocessing workflows. The FGDB implement efficiently stores spatial soil features, tabular model elements and linked relationships, while seamlessly providing the environment for the extraction, spatial analysis, and mapping of the models’ parameters. As an alternative, the composing spatial elements, polygons and multi-resolution rasters, and the models’ elements are offered as a file-folder system of data with completely Open Source formats. Finally, this geographic database coverage provides support for the traditional large-scale and harmonized application of the models as well as an alternative to the higher resolution companion for areas where this information is still under development.

Two‐Year Growth Cycle Sugarcane Crop Parameter Attributes and Their Application in Modeling

The renewed interest in the use of sugarcane (Saccharin officinarum L.) for biofuel could provide a viable market for potential Hawaiian sugarcane feedstock producers. In Hawaii, sugarcane is grown as an irrigated 2‐yr cycle crop. There is however little information on crop parameter attributes of 2‐yr cycle sugarcane. This field study on Maui, Hawaii, analyzed the relationship between sugarcane biomass accumulation and specific crop parameters. Overall, the high dry biomass yield (80.20 Mg ha−1) was the result of a high leaf area index (LAI, 7.50) and radiation use efficiency (RUE, 2.06 g MJ−1. The crop growth rate was highly correlated to LAI (R2, 0.86), and a light extinction coefficient (k) of 0.53 was estimated. Stalk density was estimated at 18 stalks m−2, with a maximum plant height of 3.6 m, and a rooting depth exceeding 2.0 m. When the crop parameters were incorporated into a biological model of Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) the model accurately simulated sugarcane yields across seven different soil types and multiple management scenarios of applied irrigation water, N and P fertilizer inputs and various planting and harvest dates. The mean simulation percent (%) errors ranged from –6.4% to 1.8%, while the calculated Fisher's paired t test of 1.41 with 39 degrees of freedom, showed no significant differences (P ≥ 0.05) between measured and simulated yields. The ALMANAC model should be useful as a decision support tool for evaluating sugarcane management alternatives that maximize yields while optimizing water, N and P inputs.

A Stochastic Approach for Predicting the Profitability of Bioenergy Grasses

Switchgrass (Panicum virgatum L.) and miscanthus (Miscanthus × giganteus) have potential to meet a growing demand for renewable energy feedstock. Before producers will invest in planting these crops, they need credible estimations of the potential profits. The objective of this study was to examine profitability of growing these perennial bioenergy grasses, incorporating uncertain prices and yields in the model. The model compared the profitability of five crops {switchgrass, miscanthus, corn [Zea mays L.], soybean [Glycine max (L.) Merr.] and tall fescue pasture [Festuca arundinacea]} on three soil profiles common in northeastern Missouri: an upland, noneroded soil, an eroded soil, and floodplain soil. The effect of the USDA Biomass Crop Assistance Program (BCAP) and crop insurance on the decision to grow switchgrass and miscanthus was analyzed. Actual grain yields, Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) modeled grass yields, and Food and Agricultural Policy Research Institute (FAPRI) baseline prices were used to simulate profit. The results were compared using cumulative distribution functions (CDFs). Corn production was always chosen by decision makers of all risk attitudes to other crops. Miscanthus was always more profitable than switchgrass. Based on the CDFs, perennial bioenergy grasses were more likely to be planted in eroded soils currently in pasture production; however, there were scenarios in which they could be planted on eroded cropland currently planted to soybean. Lastly, crop insurance for corn and soybean could affect the decision to plant perennial bioenergy grasses for risk averse producers.

Comparative Breakeven Analysis of Annual Grain and Perennial Switchgrass Cropping Systems on Claypan Soil Landscapes

The Central Claypan region is an important agricultural production contributor in the U.S. Midwest. Because of the tendency for grain yield fluctuations caused by water stress, however, claypan soils may have potential for conversion from grain to grass production in support of biomass energy markets and conservation programs. This study examined the economic potential of transitioning from grain to perennial switchgrass (Panicum virgatum L.) production on claypan soils using comparative breakeven analysis. Partial budgets for a corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation and a perennial switchgrass cropping system were developed. Yield data from research plots with varying topsoil depths defined by the underlying claypan layer were used to establish yield expectations as affected by topsoil depth. Switchgrass yield projections for the claypan region were simulated with the Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model. Comparative breakeven prices for two switchgrass cultivars ranged from US$65 on marginal, eroded soils to US$124 Mg−1 on soils with >27 cm of topsoil. Breakeven yields with a biomass price of US$40 Mg−1 would require yield increases of up to 450% for lower yielding cultivars. The switchgrass cultivar Kanlow holds the most promise for biomass production on claypan soils; with an average projected yield of 12.56 Mg ha−1, breakeven prices fall to around US$60–80 Mg−1 for marginal soils with <15 cm of topsoil. Based on these projections, perennial switchgrass shows potential to compete with conventional grain crops at reasonable yield levels on eroded soils in the Central Claypan region.

Soil and Variety Effects on the Energy and Carbon Balances of Switchgrass-Derived Ethanol

This study examined the effects of soil and switchgrass variety on sustainability and eco-friendliness of switchgrass-based ethanol production. Using the Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model, switchgrass biomass yields were simulated for several scenarios of soils and varieties. The yields were fed to the Integrated Biomass Supply Analysis and Logistics (IBSAL) model to compute energy use and carbon emissions in the biomass supply chain, which then were used to compute Net Energy Value (NEV) and Carbon Credit Balance (CCB), the indicators of sustainability and eco-friendliness, respectively. The results showed that the values of these indicators increased in the direction of heavier to lighter soils and on the order of north-upland, south-upland, north-lowland, and south-lowland varieties. The values of NEV and CCB increased in the direction of dry to wet year. Gaps among the varieties were smaller in a dry year than in a wet year. From south to north, NEV and CCB decreased for lowland varieties but increased for upland ones. Thus, the differences among the varieties decreased in the direction of lower to higher latitudes. The study demonstrated that the sustainability and eco-friendliness of switchgrass-based ethanol production could be increased with alternative soil and variety options.

The Rancher’s ALMANAC

The mathematical Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) Model simulates shortand longterm western rangeland vegetation response to various conservation strategies. The model was chosen by the Rangeland Conservation Effects Assessment Program to assess rangeland health across the western United States. Here we demonstrate the model’s accuracy as compared to NRCS Ecological Site Description data at sites in Nevada, Utah, and California. The model is free and available to the public. The USDA–ARS Grassland, Soil, and Water Research Lab at Temple, Texas (http://www.ars.usda.gov/spa/gswrl), conducts free seminars on input parameter development and ALMANAC simulation training. The United States’ western rangelands are a valuable national natural resource. Rangelands provide important ecological benefi t: storing carbon in the soils, mitigating soil loss, and supporting a diversity of plant, animal, and fungal species. Their economic services are comparably important; they support a vibrant and varied livestock industry, maintain wildlife habitat, and provide recreational opportunity to hikers, birders, wildlife photographers, and off-road enthusiasts. They also are valued by the scientifi c community, including geologists, hydrologists, plant and animal ecologists, and soil scientists, to name a few. However, these lands face mounting pressures from urban and suburban expansion, exotic species invasions, changing fi re dynamics, and increased human use. We must determine the best management strategies for these lands to maintain their sustainability for perpetuity, so that we can enjoy the resource now while maintaining it for future generations. Here we demonstrate the applicability of a promising decision support tool to help guide us towards sustainable management decisions: the ALMANAC model.

Development and optimization of an Agro-BGC ecosystem model for C 4 perennial grasses

Extrapolating simulations of bioenergy crop agro-ecosystems beyond data-rich sites requires biophysically accurate ecosystem models and careful estimation of model parameters not available in the literature. To increase biophysical accuracy we added C 4 perennial grass functionality and agricultural practices to the Biome-BGC (BioGeochemical Cycles) ecosystem model. This new model, Agro-BGC, includes enzyme-driven C 4 photosynthesis, individual live and dead leaf, stem, and root carbon and nitrogen pools, separate senescence and litter fall processes, fruit growth, optional annual seeding, flood irrigation, a growing degree day phenology with a killing frost option, and a disturbance handler that simulates nitrogen fertilization, harvest, fire, and incremental irrigation. To obtain spatially generalizable vegetation parameters we used a numerical method to optimize five unavailable parameters for Panicum virgatum (switchgrass) using biomass yield data from three sites: Mead, Nebraska, Rockspring, Pennsylvania, and Mandan, North Dakota. We then verified simulated switchgrass yields at three independent sites in Illinois (IL). Agro-BGC is more accurate than Biome-BGC in representing the physiology and dynamics of C 4 grass and management practices associated with agro-ecosystems. The simulated two-year average mature yields with single-site Rockspring optimization have Root Mean Square Errors (RMSE) of 70, 152, and 162 and biases of 43, −87, 156 g carbon m −2 for Shabbona, Urbana, and Simpson IL, respectively. The simulated annual yields in June, August, October, December, and February have RMSEs of 114, 390, and 185 and biases of −19, −258, and 147 g carbon m −2 for Shabbona, Urbana, and Simpson IL, respectively. These RMSE and bias values are all within the largest 90% confidence interval around respective IL site measurements. Twenty-four of twenty-six simulated annual yields with Rockspring optimization are within 95% confidence intervals of Illinois site measurements during the mature fourth and fifth years of growth. Ten of eleven simulated two-year average mature yields with Rockspring optimization are within 65% confidence intervals of Illinois site measurements and the eleventh is within the 95% confidence interval. Rockspring optimized Agro-BGC achieves accuracies comparable to those of two previously published models: Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) and Integrated Farm System Model (IFSM). Agro-BGC suffers from static vegetation parameters that can change seasonally and as plants age. Using mature plant data for optimization mitigates this deficiency. Our results suggest that a multi-site optimization scheme using mature plant data from more sites would be adequate for generating spatially generalizable vegetation parameters for simulating mature bioenergy crop agro-ecosystems with Agro-BGC.

Switchgrass Biomass Simulation at Diverse Sites in the Northern Great Plains of the U.S.

The Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model, originally developed and tested in Texas, needs to be tested for switchgrass (Panicum virgatum L.) simulation in more northerly locations. The Northern Great Plains of the U.S. has regionally adapted native populations of switchgrass and has excellent potential for growing switchgrass as a biofuel crop. The objective of this study was to adjust switchgrass parameters (potential leaf area index (DMLA) and degree days to maturity (PHU)) for northern sites and populations and to validate the model against switchgrass data from diverse sites in this region. Three or 4 years of measured yield data were used from a ten field sites in North Dakota (ND), South Dakota (SD), and Nebraska (NE). ALMANAC realistically simulated mean annual switchgrass yields ranging from means of 4.75 to 9.13 Mg ha �1 . Mean simulated yields were within 3%, 15%, and 9% of mean measured yields for NE, SD, and ND, respectively. Sensitivity analysis with temperature and rainfall demonstrated variable responses of potential yields depending on whether season duration, soil water, or soil nitrogen was the limiting factor at a site. ALMANAC shows promise as a useful tool for switchgrass evaluation and management in the northern Great Plains and in similar latitudes with low rainfall such as the East European Plain.

A multi-species, process based vegetation simulation module to simulate successional forest regrowth after forest disturbance in daily time step hydrological transport models

To simulate the effects of tree harvest on boreal forest catchment hydrology, the vegetation growth model within the soil and water assessment tool (SWAT) must reproduce the successional stages of forest reestablishment. The agricultural land management alternatives with numerical assessment criteria (ALMANAC), a multi-species growth model, was modified to simulate vegetation regeneration on forest sites after harvest (ALMANACBF). The model uses similar principles of vegetation growth as the current vegetation model in SWAT, and input requirements are consistent with typical forest inventory databases. This article describes the algorithms integrated into the ALMANACBF model to simulate successional stages in forest growth, provides initial estimates of parameters required to simulate multi-species forest succession, and presents examples of the type of variability in vegetation growth scenarios that these algorithms can reproduce. The model structure and modelling approach shows promise as a tool for foresters to evaluate how patterns and timing of forest management activities influence forest watershed hydrology.

Switchgrass simulation by the ALMANAC model at diverse sites in the southern US

Simulation models for plant species important for biofuel such as switchgrass (Panicum virgatum L.) can be used to make management decisions related to biomass productivity and related to environmental impacts such as soil erosion and changes in surface and groundwater quality. The present study was designed to evaluate the accuracy of simulation of switchgrass biomass production by the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) model at sites in Texas, Arkansas, and Louisiana. We used multi-year field data for Alamo switchgrass yields at each of five sites to evaluate ALMANAC. The model realistically simulated mean switchgrass yields at each of the locations and the total variability of all the data pooled, but did not perform as well in accounting for the year-to-year variability within some locations. Sensitivity analysis showed that changes in runoff curve number (CN) and changes in maximum stomatal conductance (GSI) had variable impacts on simulated values among the sites. A 15% change in CN changed mean annual biomass yield from 0% to 16% depending on location. Changing GSI from 4 to 8 mm s−1 changed mean annual biomass from 1% to 31% depending on location. ALMANAC shows promise as a tool to realistically simulate mean biomass yields and variability around the mean for multi-year runs of switchgrass at these diverse sites. Further research, with more extensive measurements of soil parameters including soil nutrients is needed to determine why the model reasonably simulated individual years’ yields at Stephenville, TX and Dallas, TX, but had difficulty at other sites.

The ALMANAC model's sensitivity to input variables

Crop models often require extensive input data sets to realistically simulate crop growth. Development of such input data sets can be difficult for some model users. The objective of this study was to evaluate the importance of variables in input data sets for crop modeling. Based on published hybrid performance trials in eight Texas counties, we developed standard data sets of 10-year simulations of maize and sorghum for these eight counties with the Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model. The simulation results were close to the measured county yields with bias values and root mean square errors less than 1.0 Mg ha −1 in each county. We then analyzed the sensitivity of grain yield to solar radiation, rainfall, soil depth, soil plant available water, and runoff curve number, comparing simulated yields with those with the original, standard data sets. Runoff curve number [US Department of Agriculture, Soil Conservation Service (1972) National Engineering Handbook, Hydrology Section 4, chapters 4–10] changes had the greatest impact on simulated maize and sorghum yields for all the counties. The next most critical input was rainfall, and then solar radiation for both maize and sorghum, especially for dryland conditions. For irrigated sorghum, solar radiation was the second most critical input instead of rainfall. The degree of sensitivity of yield to all variables was larger for maize than for sorghum except for solar radiation. Many models use a USDA curve number approach to represent soil water redistribution, so it will be important to have accurate curve numbers, rainfall, and soil depth to realistically simulate yields.

Maize and Sorghum Simulation in Diverse Texas Environments

AbstractCrop models for decision making should accurately simulate grain yields across a wide range of soils and climate regimes. This study was designed to evaluate two models' ability to simulate plot grain yields under diverse weather conditions and soils in Texas. The objective was to compare measured grain yields of maize (Zea mays L.) and sorghum [Sorghum bicolor (L.) Moench] with grain yields simulated by the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) model and to compare measured maize yields with grain yields simulated by a new version of the CERES‐Maize (Crop‐Environment Resource Synthesis) model. Using yield performance trials, both models were tested for their ability to simulate the mean yield for five years at each location and their ability to describe year‐to‐year variability in measured yields. Both models were tested at nine locations for maize and ALMANAC was tested at eight locations for sorghum. Model inputs included parameters for the soil type, planting dates, planting rates, and locally measured weather data. Mean simulated grain yield for each site was within 10% of the mean measured grain yield for all cases, except for CERES at Thrall, where mean simulated yield was 13% lower than mean measured yield. When the models did not account for a significant amount of the year‐to‐year variability in measured grain yield at a site, it was usually due to the narrow range of measured grain yields. The soils, weather, and crop parameter data sets developed here can be useful starting points for deriving data at similar sites, giving model users examples of realistic input data.

Evaluation of Two Maize Models for Nine U.S. Locations

AbstractCrop models can be evaluated based on accuracy in simulating several years' yields for one location or on accuracy in simulating long‐term mean yields for several locations. Our objective was to see how the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) model and a new version of CERES‐Maize (Crop‐Environment Resource Synthesis) simulate grain yield of rainfed maize (Zea mays L.). We tested the models at one county in each of nine states: Minnesota, New York, Iowa, Illinois, Nebraska, Missouri, Kansas, Louisiana, and Texas (MN, NY, IA, IL, NE, MO, KS, LA, and TX). Simulated grain yields were compared with grain yields reported by the National Agricultural Statistical Service (NASS) for 1983 to 1992. In each county we chose a soil commonly used in maize production, and we used measured weather data. Mean simulated grain yield for each county was always within 5% of the mean measured grain yield for the location. Within locations, measured grain yield was regressed on simulated grain yields and tested to see if the slope was significantly different from 1.0 and if the y‐intercept was significantly different from 0.0, both at the 95% confidence level. Only at MN, NY, and NE for ALMANAC and at MN, NY, and TX for CERES was slope significantly different from 1.0 or intercept significantly different from 0.0. The CVs of simulated grain yields were similar to the those of measured yields at most sites. Also, both models were appropriate for predicting an individual year's yield for most counties. Values for plant parameters, such as heat units for development and the harvest index, and values for soil parameters describing soil water‐holding capacity offer users reasonable inputs for simulating maize grain yield over a wide range of locations.

Simulating Alamo Switchgrass with the ALMANAC Model

AbstractA model for forage yield with adequate details for leaf area, biomass, nutrients, and hydrology would be valuable for making management decisions. The objectives of this study were to develop Alamo switchgrass (Panicum virgatum L.) parameters for the Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model and demonstrate its accuracy across a wide range of environments. Derived plant parameters included potential leaf area index (LAI), potential biomass growth per unit intercepted light, optimum nutrient concentrations, and growth responses to temperature. The model's simulated yields accounted for 79% of the variability in measured yields for one‐cut and two‐cut harvest systems from six diverse sites in Texas in 1993 and 1994. Simulated yields for three locations differed in sensitivity to potential LAI, heat units to maturity, radiation use efficiency (RUE), and soil depth. The ALMANAC model shows promise as a management tool for this important forage and bioenergy crop.