While biofuels have been touted as a benefit for growers—with the ability to be planted on marginal lands, for improved wildlife habitat, to sustain soils, and to reduce runoff—there remains to be a general summary of how beneficial they really are. This paper aims to review the environmental effects, feasibility, and economic aspects of using native perennial grasses in North America as biofuels as opposed to traditional annual crops. The Scopus database was used to search for manuscripts relating to each topic. In some instances, very few results appeared, so a second database, Digitop, was also used. Native perennial grasses have been found to sequester carbon and cultivating them can create a carbon sink in the soil. Overall, wildlife benefit more by having native perennial grass for biofuels planted than annual maize and having fewer harvests a season is better for wildlife over the entire year. Economically, growing native perennial grasses can be advantageous especially on marginal land, where it has a comparatively high yield. Although the second-generation biofuel supply chain is susceptible to changing market prices, it can be made more resilient and has advantages, for example resistance against the impacts of drought. Although there are many cultivars to choose from, factors like climate, soil, and genetics can provide pertinent information to match each specimen’s ideal growing conditions to the right location.

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.

Near-surface remote sensing has been used to document seasonal growth patterns (i.e. phenology) for plant communities in diverse habitats. Phenology from this source may only apply to the area within the images. Meanwhile ecosystem models can accommodate variable weather and landscape differences to plant growth, but accuracy is improved by adding ground-truthed inputs. The objective of this study was to use PhenoCam data, image analysis, and Beer’s law with established extinction coefficients to compare leaf area index (LAI) development in the ALMANAC model for diverse plant types and environments. Results indicate that PhenoCam time series imagery can be used to improve leaf area development in ALMANAC by adjusting parameter values to better match LAI derived values in new diverse environments. Soybeans, mesquite, and maize produced the most successful match between the model simulations and PhenoCam data out of the eight species simulated. This study represents, to our knowledge, the first independent evaluation of the ALMANAC process-based plant growth model with imagery in agroecosystems available from the PhenoCam network. The results show how PhenoCam data can make a valuable contribution to validate process-based models, making these models much more realistic and allows for expansion of PhenoCam influence.