A biophysical model of Sugarcane growth

Abstract

Scientists predict that global agricultural lands will expand over the next few decades due to increasing demands for food production and an exponential increase in cropbased biofuel production. These changes in land use will greatly impact biogeochemical and biogeophysical cycles across the globe. It is therefore important to develop models that can accurately simulate the interactions between the atmosphere and important crops. In this study, we develop and validate a new process-based sugarcane model (included as a module within the Agro-IBIS dynamic agro-ecosystem model) which can be applied at multiple spatial scales. At site level, the model systematically under/ overestimated the daily sensible/latent heat flux (by À10.5% and 14.8%, H and kE, respectively) when compared against the micrometeorological observations from southeast Brazil. The model underestimated ET (relative bias between À10.1% and -12.5%) when compared against an agro-meteorological field experiment from northeast Australia. At the regional level, the model accurately simulated average yield for the four largest mesoregions (clusters of municipalities) in the state of São Paulo, Brazil, over a period of 16 years, with a yield relative bias of À0.68% to 1.08%. Finally, the simulated annual average sugarcane yield over 31 years for the state of Louisiana (US) had a low relative bias (À2.67%), but exhibited a lower interannual variability than the observed yields.

Carbon isotope fractionation for cotton genotype selection

“…Means followed by the same letter in each location do not differ by at 5% probability. enhance productivity of cotton species G. hirsutum and G. barbadense, in the last 50 years, most breeding programs have focused on increases in the net rate of photosynthesis and tolerance to elevated temperatures by increasing stomatal conductance (Radin et al, 1994;Lu et al, 1998;Zhang et al, 2013). However, from a global warm perspective (Trenberth et al, 2007;Cuadra et al, 2012;Marin et al, 2013;Bowman et al, 2013), breeding programs should redirect their efforts to construct more suitable plants to face challenges in new climate scenarios. In this view, plant breeders and physiologists are concentrating their efforts in the construction of plants not only to survive under abiotic stresses but also to be stable and productive in these hard environments.…”

Effects of Land Use Change for Crops on Water and Carbon Budgets in the Midwest USA

“…The model represents trees, shrubs, and grasses, along with three major annual crops (corn, soybean, wheat), two perennial grasses managed for biofuel production (switchgrass and miscanthus), and sugarcane [12,13,19]. The model is capable of simulating crop growth and behavior by taking into account management such as irrigation, fertilizer application, planting and harvest date, and cultivar selection.…”

Influence of Soil Properties in Different Management Systems: Estimating Soybean Water Changes in the Agro-IBIS Model

“…It includes 12 natural and 6 crop plant functional types (Kucharik and Brye 2003;Vanloocke Cuadra et al 2012). Soybean (Glycine max), maize (Zea mays), wheat (Triticum vulgare Vill), Miscanthus x. giganteus, and switchgrass and sugarcane (Saccharum officinarum L.) representations are also included.…”

“…Recently, land surface models (LSMs) have been developed in order to simulate different crop types and their responses to climatic variability, resulting in improved early detection of agricultural drought with further mitigation responses (Kucharik et al 2000;Kucharik and Brye 2003;Kucharik and Twine 2007;Lokupitiya et al 2009;Cuadra et al 2012;Mo et al 2011;Ingwersen et al 2011;Crow et al 2012;Song et al 2013;Twine et al 2013). These models take into account crop phenological and physiological processes and their influence on surface water, energy, and carbon exchanges.…”