Simulating the Productivity of Desert Woody Shrubs in Southwestern Texas

In the southwestern U.S., many rangelands have converted from native grasslands to woody shrublands dominated by creosotebush (Larrea tridentate) and honey mesquite (Prosopis glandulosa), threatening ecosystem health. Both creosotebush and mesquite have well-developed long root systems that allow them to outcompete neighboring plants. Thus, control of these two invasive shrubs is essential for revegetation in arid rangelands. Simulation models are valuable tools for describing invasive shrub growth and interaction between shrubs and other perennial grasses and for evaluating quantitative changes in ecosystem properties linked to shrub invasion and shrub control. In this study, a hybrid and multiscale modeling approach with two process-based models, ALMANAC and APEX was developed. Through ALMANAC application, plant parameters and growth cycles of creosotebush and mesquite were characterized based on field data. The developed shrub growth curves and parameters were subsequently used in APEX to explore productivity and range condition at a larger field scale. APEX was used to quantitatively evaluate the effect of shrub reductions on vegetation and water and soil qualities in various topological conditions. The results of this study showed that this multi modeling approach is capable of accurately predicting the impacts of shrubs on soil water resources

Simulating diverse native C4 perennial grasses with varying rainfall

AbstractRainfall is recognized as a major factor affecting the rate of plant growth development. The impact of changes in amount and variability of rainfall on growth and production of different forage grasses needs to be quantified to determine how climate change can impact rangelands. Comparative studies to evaluate the growth of several perennial forage species at different rainfall rates will provide useful information by identifying forage management strategies under various rainfall scenarios. In this study, the combination of rainfall changes and soil types on the plant growth of 10 perennial forage species was investigated with both the experimental methods, using rainout shelters, and with the numerical methods using the plant growth simulation model, ALMANAC. Overall, most species significantly increased basal diameter and height as rainfall increased. Like measured volume, simulated yields for all species generally increased as rainfall increased. But, large volume and yield increases were only observed between 350 and 850 mm/yr. Simulating all species growing together competing agrees relatively well with observed plant volumes at low rainfall treatment, while simulating all species growing separately was slightly biased towards overestimation on low rainfall effect. Both simulations agree relatively well with observed plant volume at high rainfall treatment

Forage Yield Estimation with a Process-Based Simulation Model

A process-based simulation model of natural grasslands and improved pastures can be used to compare mean productivity and stability of forage productivity across years, agroecological regions, and management approaches. Model simulations can help farmers develop management practices to optimize livestock stocking rates and nutrient management for native and improved grasses on different soils with varying rainfall amounts. Likewise, forages are adapted to a wide variety of soils, rainfall zones, and latitudes. The objective of this chapter is to describe the Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model that simulates a wide variety of environmental and management impacts on forage production, soil health, and conservation concerns, including nutrient and sediment losses. We describe the various processes simulated in the model and input data requirements. We also describe how to derive plant parameters for various forage plant species. The model has been applied to simulate forage yields across years and diverse environments in the U.S. and tested using published forage yield data from Natural Resources Conservation Service, United States Dept. of Agric. Many common native and introduced grasses or grass mixtures in the U.S. have been successfully simulated. We also describe and discuss knowledge gaps for the model that future research should address to improve this and similar simulation models

Advancement of a Soil Parameters Geodatabase for the Modeling Assessment of Conservation Practice Outcomes in the United States

US-ModSoilParms-TEMPLE is a database composed of a set of geographic databases functionally storing soil-spatial units and soil hydraulic, physical, and chemical parameters for three agriculture management simulation models, SWAT, APEX, and ALMANAC. This paper introduces the updated US-ModSoilParms-TEMPLE, which covers the entire United States and is organized as a framework of 22 nested and hydrologically-ordered regional geographic databases with internal spatial segmentation drainage-defined at a conveniently manageable tile (Watershed Boundary Dataset’s, WBD, 8-digit Subbasin) level. Spatial features are stored in multiple formats (raster and vector) and resolutions (10-meter and 30-meter), while being in direct relationship with the table of attributes storing the models’ parameters. A significant number of former parameter voids, determined by the local incompleteness of the source datasets, were filled using a methodology leveraging upon the hierarchy of the Soil Taxonomy information and the geographic location of the gaps. The functionality of each geographic database was extended by adding customized tools, which streamline the incorporation into geoprocessing workflows, the aggregation and extraction of data sets, and finally the export to other model support software user environments. These tools are attached and conveniently distributed along with detailed metadata documentation within each of the developed regional geographic databases. The system hosting this framework is developed using a proprietary software format (ESRI® File Geodatabase), however, a companion version of the framework of 8-digit tiles is also developed and provided using openly accessible formats. The experience shared in this paper might help other efforts in developing hydrology-oriented geographical databases

A Soil Parameters Geodatabase for the Modeling Assessment of Agricultural Conservation Practices Effects in the United States

Soil parameters for hydrology modeling in cropland dominated areas, from the regional to local scale, are part of critical biophysical information whose deficiency may increase the uncertainty of simulated conservation effects and predicting potential. Despite this importance, soil physical and hydraulic parameters lack common, wide-coverage repositories combined to digital maps as required by various hydrology-based agricultural water quality models. This paper describes the construction of a geoprocessing workflow and the resultant hydrology-structured soil hydraulic, physical, and chemical parameters geographic database for the entire United States, named US-SOILM-CEAP. This database is designed to store a-priori values for a suit of models, such as SWAT (Soil and Water Assessment Tool), APEX (Agricultural Policy Environmental EXtender) and ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria), which are commonly used for the across scale assessment of agricultural hydrology and conservation practice scenarios. The Soil Survey Geographic (SSURGO) database developed by the U.S. Department of Agriculture provided the main source data for this development. Additional spatial information, a geographic information system platform and Python computer programming language code were used to create hydrology-based tile coverage of the areal soil units linked to the specific and detailed attributes required by each model. The created repository adds value to the source soil survey data, while maintaining and extending the detailed information necessary for the across scale and combined application of the models. Ultimately, our multi-model database provides a comprehensive product achieving joined informational-mapping-geoprocessing functionality with the explicit maintenance of the original conceptual links between soil series and composing soil layers, allowing for efficient data retrieval, analysis and service as input for modeling conservation effects

Similarity of maize seed number responses for a diverse set of sites

Accurate modeling of maize (Zea mays L.) yields in diverse environments requires realistic simulation of seed numbers. Response of maize seed number to growth or light interception soon after pollination has been described with different types of functions. The objective of this study was to compare maize seed number responses to intercepted solar radiation or growth with data from a diverse set of sites. Pioneer hybrid 3394 planted near Temple, TX in 1999 at 2.5 to 20 plants$\cdot$m$^{-2}$ showed a linear function for seed number responses to light intercepted per plant in the 11 d following silking and to ear growth rate in these 11 d. Similar linear seed number responses were found for three hybrids in Canada at 4 to 13 plants$\cdot$m$^{-2}$. Likewise, the function for Pioneer 3394 in Temple was found to be similar to a regression for the same hybrid grown in Pennsylvania, and was similar to a function developed in Kenya. Thus, under the diverse environmental conditions of these studies, linear seed number functions appeared reasonable at these sites. Such seed number functions are critical to the understanding of optimization of planting density to maximum seed production per unit ground area. In the absence of drought stress, the optimum density will be the minimum planting density which could attain near-complete light interception at silking. As the probability of drought stress increases due to decreased soil water holding capacity or decreased expected rainfall, the optimum density would decrease accordingly.Similarité des réponses du nombre de grains par épi de maïs pour un jeu de sites variés. La modélisation précise des rendements du maïs (Zea mays L.) dans divers environnements nécessite une simulation réaliste du nombre de grains par épi. La réponse du nombre de grains à la croissance ou à l'interception de la lumière aussitôt après la pollinisation a été décrite avec différents types de fonctions. L'objectif de cette étude était de comparer les réponses du nombre de grains par épi au rayonnement solaire intercepté ou à la croissance de l'épi avec les données provenant d'un ensemble de sites variés. L'hybride Pioneer 3394 planté près de Temple (Texas, USA) en 1999 avec 2,5 à 20 plants$\cdot$m$^2$ a montré une relation linéaire entre le nombre de grains et la lumière interceptée durant les 11 jours qui ont suivi la sortie des soies ainsi qu'avec le taux de croissance de l'épi durant ces mêmes 11 jours. Des réponses linéaires similaires ont été trouvées pour trois hybrides au Canada avec 4 à 13 plants$\cdot$m$^2$. De même, la relation pour Pioneer 3394 à Temple a été trouvée similaire à celle obtenue pour le même hybride cultivé en Pennsylvanie ainsi qu'au Kenya. Ainsi, sous diverses conditions d'environnement de ces études, une relation linéaire avec le nombre de grains apparaît comme raisonnable dans ces sites. De telles relations linéaires avec le nombre de grains sont critiques pour appréhender l'optimisation de la densité de plantation afin d'atteindre le maximum de production de grains par unité de surface de sol. En l'absence de stress dû à la sécheresse, la densité optimale sera la densité de plantation minimale qui permettrait d'atteindre l'interception pratiquement complète du rayonnement au moment de la sortie des soies. Comme la probabilité de stress hydrique augmente avec la décroissance de la capacité de rétention en eau du sol ou décroît avec les précipitations escomptées, la densité optimale devra décroître en tenant compte de ces facteurs

Tropical Tree Crop Simulation with a Process-Based, Daily Timestep Simulation Model (ALMANAC): Description of Model Adaptation and Examples with Coffee and Cocoa Simulations

Coffee (Coffea species) and Cocoa (Theobroma cacao) are important cash crops grown in the tropics but traded globally. This study was conducted to apply the ALMANAC model to these crops for the first time, and to test its ability to simulate them under agroforestry management schemes and varying precipitation amounts. To create this simulation, coffee was grown on a site in Kaua’i, Hawai’i, USA, and cocoa was grown on a site in Sefwi Bekwai, Ghana. A stand-in for a tropical overstory tree was created for agroforestry simulations using altered parameters for carob, a common taller tropical tree for these regions. For both crops, ALMANAC was able to realistically simulate yields when compared to the collected total yield data. On Kaua’i, the mean simulated yield was 2% different from the mean measured yield, and in all three years, the simulated values were within 10% of the measured values. For cocoa, the mean simulated yield was 3% different from the mean measured yield and the simulated yield was within 10% of measured yields for all four available years. When precipitation patterns were altered, in Ghana, the wetter site showed lower percent changes in yield than the drier site in Hawai’i. When agroforestry-style management was simulated, a low Leaf Area Index (LAI) of the overstory showed positive or no effect on yields, but when LAI climbed too high, the simulation was able to show the detrimental effect this competition had on crop yields. These simulation results are supported by other literature documenting the effects of agroforestry on tropical crops. This research has applied ALMANAC to new crops and demonstrated its simulation of different management and environmental conditions. The results show promise for ALMANAC’s applicability to these scenarios as well as its potential to be further tested and utilized in new circumstances