Climate smart agriculture and global food-crop production.

Most business-as-usual scenarios for farming under changing climate regimes project that the agriculture sector will be significantly impacted from increased temperatures and shifting precipitation patterns. Perhaps ironically, agricultural production contributes substantially to the problem with yearly greenhouse gas (GHG) emissions of about 11% of total anthropogenic GHG emissions, not including land use change. It is partly because of this tension that Climate Smart Agriculture (CSA) has attracted interest given its promise to increase agricultural productivity under a changing climate while reducing emissions. Considerable resources have been mobilized to promote CSA globally even though the potential effects of its widespread adoption have not yet been studied. Here we show that a subset of agronomic practices that are often included under the rubric of CSA can contribute to increasing agricultural production under unfavorable climate regimes while contributing to the reduction of GHG. However, for CSA to make a significant impact important investments and coordination are required and its principles must be implemented widely across the entire sector.

Effect of Donor Building Blocks on the Charge-Transfer Characteristics of Diketopyrrolopyrrole-Based Donor-Acceptor-Type Semiconducting Copolymers.

We investigate the effect of donor (D) building blocks on the charge transportation characteristics of donor (D)-acceptor (A)-type semiconducting copolymers with alternating electron-donating and electron-accepting units to provide a basis for the rational design of high-performance semiconducting polymers. For this purpose, we studied three different diketopyrrolopyrrole (DPP)-based semiconducting copolymers comprising a common dithienyl-DPP [3,6-dithienyl-2,5-diketopyrrolo(3,4-c)pyrrole] and variable donor moieties: phenylene (P)-PDPPTPT, thiophene (T)-PDPP3T, and thienothiophene (TT)-PDPP2T-TT. Structural analysis using grazing incidence X-ray diffraction indicates that all three DPP-based copolymer films have edge-on phases but poor crystallinity of the films, except the PDPP2T-TT copolymer with branched alkyl side chains that are relatively long. The electrical measurements show that the DPP-based copolymer with a TT donor unit has the highest field-effect mobility value of 0.30 cm2/V s. To understand the role of the donor units in DPP-based D-A copolymers, further insight into the charge transportation behavior is realized by analyzing the temperature-dependent transfer curves of the DPP semiconducting copolymer-based field-effect transistors using the Gaussian disorder model. Compared to the DPP-based D-A-type semiconducting copolymer with a P-moiety and shorter-branched alkyl side chains that exhibit a broad distribution in the density of localized states (DOS) and a higher thermal-activated energy for charge hopping, the DPP copolymers with a TT-moiety and longer branched side chains have the narrowest DOS, the lowest activation energy, and thus the highest hole mobility. These results suggest that the higher mobilities obtained from PDPP2T-TT with a TT donor unit can be attributed to the suppressed DOS distribution near the transport level, which mainly originates from the narrowest energy band gap tuned with the orbital couplings of the DPP acceptor and TT donor units.

Land-use change and greenhouse gas emissions from corn and cellulosic ethanol.

BACKGROUND: The greenhouse gas (GHG) emissions that may accompany land-use change (LUC) from increased biofuel feedstock production are a source of debate in the discussion of drawbacks and advantages of biofuels. Estimates of LUC GHG emissions focus mainly on corn ethanol and vary widely. Increasing the understanding of LUC GHG impacts associated with both corn and cellulosic ethanol will inform the on-going debate concerning their magnitudes and sources of variability. RESULTS: In our study, we estimate LUC GHG emissions for ethanol from four feedstocks: corn, corn stover, switchgrass, and miscanthus. We use new computable general equilibrium (CGE) results for worldwide LUC. U.S. domestic carbon emission factors are from state-level modelling with a surrogate CENTURY model and U.S. Forest Service data. This paper investigates the effect of several key domestic lands carbon content modelling parameters on LUC GHG emissions. International carbon emission factors are from the Woods Hole Research Center. LUC GHG emissions are calculated from these LUCs and carbon content data with Argonne National Laboratory's Carbon Calculator for Land Use Change from Biofuels Production (CCLUB) model. Our results indicate that miscanthus and corn ethanol have the lowest (-10 g CO2e/MJ) and highest (7.6 g CO2e/MJ) LUC GHG emissions under base case modelling assumptions. The results for corn ethanol are lower than corresponding results from previous studies. Switchgrass ethanol base case results (2.8 g CO2e/MJ) were the most influenced by assumptions regarding converted forestlands and the fate of carbon in harvested wood products. They are greater than miscanthus LUC GHG emissions because switchgrass is a lower-yielding crop. Finally, LUC GHG emissions for corn stover are essentially negligible and insensitive to changes in model assumptions. CONCLUSIONS: This research provides new insight into the influence of key carbon content modelling variables on LUC GHG emissions associated with the four bioethanol pathways we examined. Our results indicate that LUC GHG emissions may have a smaller contribution to the overall biofuel life cycle than previously thought. Additionally, they highlight the need for future advances in LUC GHG emissions estimation including improvements to CGE models and aboveground and belowground carbon content data.

Modeling of phosphorus loads in sugarcane in a low-relief landscape using ontology-based simulation.

Water flow and P dynamics in a low-relief landscape manipulated by extensive canal and ditch drainage systems were modeled utilizing an ontology-based simulation model. In the model, soil water flux and processes between three soil inorganic P pools (labile, active, and stable) and organic P are represented as database objects. And user-defined relationships among objects are used to automatically generate computer code (Java) for running the simulation of discharge and P loads. Our objectives were to develop ontology-based descriptions of soil P dynamics within sugarcane- (Saccharum officinarum L.) grown farm basins of the Everglades Agricultural Area (EAA) and to calibrate and validate such processes with water quality monitoring data collected at one farm basin (1244 ha). In the calibration phase (water year [WY] 99-00), observed discharge totaled 11,114 m3 ha(-1) and dissolved P 0.23 kg P ha(-1); and in the validation phase (WY 02-03), discharge was 10,397 m3 ha(-1) and dissolved P 0.11 kg P ha(-). During WY 99-00 the root mean square error (RMSE) for monthly discharge was 188 m3 ha(-1) and for monthly dissolved P 0.0077 kg P ha(-1); whereas during WY 02-03 the RMSE for monthly discharge was 195 m3 ha(-1) and monthly dissolved P 0.0022 kg P ha(-1). These results were confirmed by Nash-Sutcliffe Coefficient of 0.69 (calibration) and 0.81 (validation) comparing measured and simulated P loads. The good model performance suggests that our model has promise to simulate P dynamics, which may be useful as a management tool to reduce P loads in other similar low-relief areas.