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Examining the Link Between Food Prices and Food Insecurity: a Multi-Level Analysis of Maize Price and Birthweight in Kenya
In developing countries where many poor people rely on rainfed, locally produced food for the majority of their caloric intake, shifts in climate and weather patterns can dramatically reduce agricultural productivity. The reduction in agricultural productivity reduces overall food availability and ultimately impacts food accessibility, putting millions of people at risk for malnutrition. In this project we focus on Kenya where roughly a third of households are food insecure. We examine the relationship of the price of maize and low birth weight to help quantify the impact of local food prices on one outcome of household food insecurity. Using spatially referenced data from recent Kenyan Demographic and Health Survey datasets, price data, livelihood information, and a remotely sensed-based measure of local growing season productivity, we develop a dataset linking pregnancies occurring from 2001 to 2008 to the spatially and temporally relevant maize price data. We construct several regression models to examine the impact of local maize prices and remotely sensed based estimates of crop production on infant birth weight - specifically low birth weight. The results of the models highlight the importance of including community crop production to evaluate maize price impacts on low birth weight outcomes. Also, because of the positive correlation between pre-pregnancy maize prices and birth weight, the results suggest that some households may benefit from high prices or that high prices may impact the number of conceptions. More generally, our work demonstrates that multilevel models that account for community-level variation are important for disentangling these complex relationships and can contribute to the discussion of how to design more effective food policies.
Effects of residue removal and tillage on greenhouse gas emissions in continuous corn systems as simulated with RZWQM2
Agricultural production is a major source of carbon dioxide (CO 2 ) and nitrous oxide (N 2 O) globally. The effects of conservation practices on soil CO 2 and N 2 O emissions remain a high degree of uncertainty. In this study, soil CO 2 and N 2 O emissions under different residue and tillage practices in an irrigated, continuous corn system, were investigated using the Root Zone Water Quality Model (RZWQM2). Combinations of no/high stover removal (NR and HR, respectively) and no-till/conventional tillage (NT and CT, respectively) field experiments were tested over the four crop-years (Apr. 2011–Apr. 2015). The model was calibrated using the NRCT, and validated with other treatments. The simulation results showed that soil volumetric water content (VWC) in the NR treatments (i.e., NRCT and NRNT) was 1.3%–1.9% higher than that in the HR treatments (i.e., HRCT and HRNT) averaged across the four years. A higher amount of CO 2 and N 2 O emissions were simulated in the NRCT across the four years (annual average: 7034 kg C/ha/yr for CO 2 and 3.8 kg N/ha/yr for N 2 O), and lower emissions were in the HRNT (annual average: 6329 kg C/ha/yr and 3.7 kg N/ha/yr for N 2 O). A long-term simulation (2001–2015) suggested that the CO 2 and N 2 O emissions were closely correlated with the stover removal degree (SRD), tillage, VWC, soil temperature (ST), years in management (Y), and fertilizer application. Stover and tillage practices had cumulative effects on CO 2 emissions. The simulated annual CO 2 emissions in 1st year from NRCT, NRNT, and HRCT were 7.8%, 0.0%, and 7.7% higher than that from HRNT, respectively; then the emissions in 15th year were 63.6%, 47.7%, and 29.1% higher, respectively. Meanwhile, there were no cumulative effects on N 2 O emissions. Furthermore, the results also demonstrated that the RZWQM2 is a promising tool for evaluating the long-term effects of CO 2 and N 2 O emissions on different conservation practices.
Iron incorporation both intra- and extra-cellularly improves the yield and saccharification of switchgrass (Panicum virgatum L.) biomass
Abstract Background Pretreatments are commonly used to facilitate the deconstruction of lignocellulosic biomass to its component sugars and aromatics. Previously, we showed that iron ions can be used as co-catalysts to reduce the severity of dilute acid pretreatment of biomass. Transgenic iron-accumulating Arabidopsis and rice plants exhibited higher iron content in grains, increased biomass yield, and importantly, enhanced sugar release from the biomass. Results In this study, we used intracellular ferritin (FerIN) alone and in combination with an improved version of cell wall-bound carbohydrate-binding module fused iron-binding peptide (IBPex) specifically targeting switchgrass, a bioenergy crop species. The FerIN switchgrass improved by 15% in height and 65% in yield, whereas the FerIN/IBPex transgenics showed enhancement up to 30% in height and 115% in yield. The FerIN and FerIN/IBPex switchgrass had 27% and 51% higher in planta iron accumulation than the empty vector (EV) control, respectively, under normal growth conditions. Improved pretreatability was observed in FerIN switchgrass (~ 14% more glucose release than the EV), and the FerIN/IBPex plants showed further enhancement in glucose release up to 24%. Conclusions We conclude that this iron-accumulating strategy can be transferred from model plants and applied to bioenergy crops, such as switchgrass. The intra- and extra-cellular iron incorporation approach improves biomass pretreatability and digestibility, providing upgraded feedstocks for the production of biofuels and bioproducts.
Economics of Crop Rotations With and Without Carinata for Sustainable Aviation Fuel Production in the SE United States
In 2019, the aviation sector in the United States emitted 255 million metric tons of carbon dioxide (CO 2 ) emissions, i.e., about five percent of the total domestic CO 2 emissions from the energy sector. The sustainable aviation fuel (SAF) derived from carinata (Brassica carinata) could reduce CO 2 emissions of the aviation sector in the United States. Therefore, it is important to estimate changes in farm economics with and without carinata for ascertaining its production feasibility. In this context, the current study first assesses a combination of 12 popular rotations of corn, cotton, peanut, and soybean with winter crops of winter wheat and carinata in South Georgia over 4 years. Then, the net present values (NPVs) of 292 feasible cropping systems over 4 years are calculated. Finally, this study develops a risk model for ascertaining the probability distributions of NPVs for selected cropping systems subject to uncertainties related to prices and yields of summer and winter crops. Carinata in the corn-corn-soybean rotation has the highest NPV ($\$$2,996/ha). The least risky rotation is cotton-cotton-peanut, with a 58.9% probability of a positive NPV. Carinata can decrease the risk level of crop rotations by 8.1%, only if a contract price of $\$$440.9/t is offered. Therefore, a risk averse, risk neutral, or risk acceptant farmer can potentially include carinata in the rotation. Overall, carinata would increase the profitability of farm operations and decrease risk in the SE United States, and therefore, a high likelihood exists, that farmers would adopt it for meeting the growing demand for SAF in the United States.
Understanding the Radiant Scattering Behavior of Vegetated Scenes
Knowledge of the physics of the scattering behavior of vegetation will ultimately serve the remote sensing and earth science community in many ways. For example, it will provide: (1) insight and guidance in developing new extraction techniques of canopy characteristics, (2) a basis for better interpretation of off-nadir satellite and aircraft data, (3) a basis for defining specifications of future earth observing sensor systems, and (4) a basis for defining important aspects of physical and biological processes of the plant system. The overall objective of the three-year study is to improve our fundamental understanding of the dynamics of directional scattering properties of vegetation canopies through analysis of field data and model simulation data. The specific objectives are to: (1) collect directional reflectance data covering the entire exitance hemisphere for several common vegetation canopies with various geometric structure (both homogeneous and row crop structures), (2) develop a scene radiation model with a general mathematical framework which will treat 3-D variability in heterogeneous scenes and account for 3-D radiant interactions within the scene, (3) conduct validations of the model on collected data sets, and (4) test and expand proposed physical scattering mechanisms involved in reflectance distribution dynamics by analyzing both field and modeling data.
Area estimation using multiyear designs and partial crop identification
Progress is reported for the following areas: (1) estimating the stratum's crop acreage proportion using the multiyear area estimation model; (2) assessment of multiyear sampling designs; and (3) development of statistical methodology for incorporating partially identified sample segments into crop area estimation.
Future impacts of ozone driven damages on agricultural systems
Current ozone (O 3 ) concentration levels entail significant damages in crop yields around the world. The reaction of the emitted precursors (mostly methane and nitrogen oxides) with solar radiation contribute to O 3 levels that exceed established thresholds for crop damage. This paper shows current and projected (up to 2080) relative yield losses (RYLs) driven by O 3 exposure for different crops and the associated economic damages applying dynamic crop production and prices that are calculated per region and period. We adjust future crop yields in the Global Change Assessment Model (GCAM) to reflect the RYLs and analyze the effects on agricultural markets. We find that the changes (generally reductions) in O 3 precursor emissions in a reference scenario would reduce the agricultural damages, compared to present, for most of the regions, with a few exceptions including India, where higher future O3 concentrations have large negative impacts on crop yields. The annual economic impact of O3 driven losses from 2010-2080 are, in billion US dollars at 2015 prices ($B), 5.0-6.0, 9.8-18.8, 6.7-10.6 and 10.4-12.5 for corn, soybeans, rice and wheat, respectively, with the large losses for wheat and soybeans driven by their comparatively high responses to O 3 . When O 3 effects are explicitly modelled as exogenous yield shocks in future periods, there is a direct impact in future agricultural markets. Therefore, the aggregated net present value (NPV) of crop production would be reduced around by $90.8B at a global level. However, these changes are not distributed evenly across regions, and the net present market value of the crops would increase by up to $118.2B (India) or decrease by up to $59.2B (China).
Brassica rapa Domestication: Untangling Wild and Feral Forms and Convergence of Crop Morphotypes
The study of domestication contributes to our knowledge of evolution and crop genetic resources. Human selection has shaped wild Brassica rapa into diverse turnip, leafy, and oilseed crops. Despite its worldwide economic importance and potential as a model for understanding diversification under domestication, insights into the number of domestication events and initial crop(s) domesticated in B. rapa have been limited due to a lack of clarity about the wild or feral status of conspecific noncrop relatives. To address this gap and reconstruct the domestication history of B. rapa, we analyzed 68,468 genotyping-by-sequencing-derived single nucleotide polymorphisms for 416 samples in the largest diversity panel of domesticated and weedy B. rapa to date. To further understand the center of origin, we modeled the potential range of wild B. rapa during the mid-Holocene. Our analyses of genetic diversity across B. rapa morphotypes suggest that noncrop samples from the Caucasus, Siberia, and Italy may be truly wild, whereas those occurring in the Americas and much of Europe are feral. Clustering, tree-based analyses, and parameterized demographic inference further indicate that turnips were likely the first crop type domesticated, from which leafy types in East Asia and Europe were selected from distinct lineages. These findings clarify the domestication history and nature of wild crop genetic resources for B. rapa, which provides the first step toward investigating cases of possible parallel selection, the domestication and feralization syndrome, and novel germplasm for Brassica crop improvement.
Biological Design of Lemnaceae aquatic plants for biodiesel production (Final Technical Report)
Support from this award enabled us to expand critical genomic and epigenomic resources available for the study and engineering of duckweeds, and to complete design-build-test-learn cycles that culminated in the engineering of high oil duckweeds. We shared our findings with the public and scientific community in a series of peer-reviewed publications highlighted within. We successfully reengineered metabolism, dramatically increasing oil content by diverting a substantial portion of accumulated carbon from starch. This sets the stage for the use of fast growing aquatic duckweeds as robust, highly productive sources of biodiesel, with the capacity to outperform soybean oil crops.
Effective Tree Scattering at L-Band
For routine microwave Soil Moisture (SM) retrieval through vegetation, the tau-omega [1] model [zero-order Radiative Transfer (RT) solution] is attractive due to its simplicity and eases of inversion and implementation. It is the model used in baseline retrieval algorithms for several planned microwave space missions, such as ESA's Soil Moisture Ocean Salinity (SMOS) mission (launched November 2009) and NASA's Soil Moisture Active Passive (SMAP) mission (to be launched 2014/2015) [2 and 3]. These approaches are adapted for vegetated landscapes with effective vegetation parameters tau and omega by fitting experimental data or simulation outputs of a multiple scattering model [4-7]. The model has been validated over grasslands, agricultural crops, and generally light to moderate vegetation. As the density of vegetation increases, sensitivity to the underlying SM begins to degrade significantly and errors in the retrieved SM increase accordingly. The zero-order model also loses its validity when dense vegetation (i.e. forest, mature corn, etc.) includes scatterers, such as branches and trunks (or stalks in the case of corn), which are large with respect to the wavelength. The tau-omega model (when applied over moderately to densely vegetated landscapes) will need modification (in terms of form or effective parameterization) to enable accurate characterization of vegetation parameters with respect to specific tree types, anisotropic canopy structure, presence of leaves and/or understory. More scattering terms (at least up to first-order at L-band) should be included in the RT solutions for forest canopies [8]. Although not really suitable to forests, a zero-order tau-omega model might be applied to such vegetation canopies with large scatterers, but that equivalent or effective parameters would have to be used [4]. This requires that the effective values (vegetation opacity and single scattering albedo) need to be evaluated (compared) with theoretical definitions of these parameters. In a recent study [9], effective vegetation opacity of coniferous trees was compared with two independent estimates of the same parameter. First, a zero-order RT model was fitted to multiangular microwave emissivity data in a least-square sense to provide effective vegetation optical depth as done in spaceborne retrieval algorithms. Second, a ratio between radar backscatter measurements with a corner reflector under trees and in an open area was calculated to obtain measured tree propagation characteristics. Finally, the theoretical propagation constant was determined by forward scattering theorem using detailed measurements of size/angle distributions and dielectric constants of the tree constituents (trunk, branches, and needles). Results indicated that the effective attenuation values are smaller than but of similar magnitude to both the theoretical and measured values. This study will complement the previous work [9] and will focus on characterization of effective scattering albedo by assuming that effective vegetation opacity is same as theoretical opacity. The resultant effective albedo will not be the albedo of single forest canopy element anymore, but it becomes a global parameter, which depends on all the processes taking place within the canopy including multiple scattering as described.
Assessing Nutrient and Carbon Responses to Agricultural Conservation Practices in Two Midwest Watersheds
The United States is undertaking efforts to transition to a low-carbon economy in response to the heightened impacts of climate change, which are largely attributed to decades of carbon-intensive development. A concerted effort by the energy sector to achieve net-zero carbon emissions is aimed at strategically reducing the carbon footprint of the energy supply chain, particularly in the production of feedstocks for biofuels. The production of biofuels relies heavily on land use and management practices in biomass cultivation. Among other factors, soil organic carbon (SOC) plays a crucial role in the biofuel carbon cycle, impacting land productivity, greenhouse gas emissions, and water quality. The process of carbon drawdown during plant growth and storage helps to mitigate the release of carbon dioxide (CO 2 ) into the atmosphere. Another key parameter is the release of nitrous oxide (N 2 O) — a potent greenhouse gas with a global warming potential 273 times that of CO 2 — from soil. While prioritizing low-carbon production, sustainable bioenergy also necessitates improved water quality and ecosystem services. Agricultural conservation practices can contribute to enhanced soil health and reduce nutrient and sediment loss into streams. However, studies that explore the broader impact of these practices on soil carbon storage and N 2 O emissions at a watershed scale are limited. Specifically, our understanding of how low-carbon feedstock production and management affect nutrient cycle dynamics is incomplete. This study assesses watershed responses to land management practices in two agriculturally dominant watersheds: the Raccoon River watershed and the Southfork of Iowa River watershed. The study focuses on carbon and nutrient dynamics, characterizing spatial and temporal variations in SOC, N 2 O, and nutrient loadings to examine the relationship between nutrient and carbon responses and agricultural conservation practices. The study employs the newly developed Soil Water Analysis Tool for Carbon (SWAT-C) model, calibrated using 20 years’ of climate and water monitoring data, to simulate and evaluate two agricultural conservation practices: no-till and crop residue harvest with cover crop planting. We compared the calibrated SWAT-C model with a historical baseline model, which allowed us to analyze various aspects of the watershed, including stream flow, suspended sediments, nitrogen, phosphorus, organic carbon, SOC at different depths, and N 2 O emissions from topsoil. Our SWAT-C modeling results indicate that, compared with results from the historical baseline model, no-till practices are positively correlated with SOC accumulation, reduced N 2 O emissions, and decreased soil loss. We also observed no change or slightly increased nitrogen and phosphorus loss to water bodies in the watersheds compared with the baseline. Conversely, crop residue harvest with cover crop planting improved water quality by reducing nutrient and soil losses but also increased N 2 O emissions. This SWAT-C modeling study establishes the groundwork for further smaller-scale and/or sub-basin-level assessments of nitrogen, phosphorus, and carbon cycles. It provides valuable science-based insights to inform policy decisions — particularly in the context of transitioning to biofuel feedstock production in these watersheds — by addressing concerns about nutrient exports to local water bodies and, ultimately, the Mississippi River.
Heat stress on agricultural workers exacerbates crop impacts of climate change
Abstract The direct impacts of climate change on crop yields and human health are individually well-studied, but the interaction between the two have received little attention. Here we analyze the consequences of global warming for agricultural workers and the crops they cultivate using a global economic model (GTAP) with explicit treatment of the physiological impacts of heat stress on humans’ ability to work. Based on two metrics of heat stress and two labor functions, combined with a meta-analysis of crop yields, we provide an analysis of climate, impacts both on agricultural labor force, as well as on staple crop yields, thereby accounting for the interacting effect of climate change on both land and labor. Here we analyze the two sets of impacts on staple crops, while also expanding the labor impacts to highlight the potential importance on non-staple crops. We find, worldwide, labor and yield impacts within staple grains are equally important at +3 ∘ C warming, relative to the 1986–2005 baseline. Furthermore, the widely overlooked labor impacts are dominant in two of the most vulnerable regions: sub-Saharan Africa and Southeast Asia. In those regions, heat stress with 3 ∘ C global warming could reduce labor capacity in agriculture by 30%–50%, increasing food prices and requiring much higher levels of employment in the farm sector. The global welfare loss at this level of warming could reach $136 billion, with crop prices rising by 5%, relative to baseline.
Bayesian Spatial Models for Projecting Corn Yields
Climate change is predicted to impact corn yields. Previous studies analyzing these impacts differ in data and modeling approaches and, consequently, corn yield projections. We analyze the impacts of climate change on corn yields using two statistical models with different approaches for dealing with county-level effects. The first model, which is novel to modeling corn yields, uses a computationally efficient spatial basis function approach. We use a Bayesian framework to incorporate both parametric and climate model structural uncertainty. We find that the statistical models have similar predictive abilities, but the spatial basis function model is faster and hence potentially a useful tool for crop yield projections. We also explore how different gridded temperature datasets affect the statistical model fit and performance. Compared to the dataset with only weather station data, we find that the dataset composed of satellite and weather station data results in a model with a magnified relationship between temperature and corn yields. For all statistical models, we observe a relationship between temperature and corn yields that is broadly similar to previous studies. We use downscaled and bias-corrected CMIP5 climate model projections to obtain detrended corn yield projections for 2020–2049 and 2069–2098. In both periods, we project a decrease in the mean corn yield production, reinforcing the findings of other studies. However, the magnitude of the decrease and the associated uncertainties we obtain differ from previous studies.
The Large Area Crop Inventory Experiment
Determination of wheat acreages in the central U.S., as well as the development and testing of yield models, is discussed in the framework of the Large Area Crop Inventory Experiment (LACIE). Particular attention is given to the goal of obtaining a 90% accuracy in yield forecasts in nine out of ten years. Current results of LACIE indicate that Landsat remote sensing data, in conjunction with information from agricultural meteorological surveys, can provide highly accurate early-season and at-harvest yield estimates in the principal wheat-growing regions of the world.
A three-part geometric model to predict the radar backscatter from wheat, corn, and sorghum
A model to predict the radar backscattering coefficient from crops must include the geometry of the canopy. Radar and ground-truth data taken on wheat in 1979 indicate that the model must include contributions from the leaves, from the wheat head, and from the soil moisture. For sorghum and corn, radar and ground-truth data obtained in 1979 and 1980 support the necessity of a soil moisture term and a leaf water term. The Leaf Area Index (LAI) is an appropriate input for the leaf contribution to the radar response for wheat and sorghum, however the LAI generates less accurate values for the backscattering coefficient for corn. Also, the data for corn and sorghum illustrate the importance of the water contained in the stalks in estimating the radar response.
Evapotranspiration Based Irrigation Trials Examine Water Requirement, Nitrogen Use, and Yield of Romaine Lettuce in the Salinas Valley
Cool season vegetables require adequate soil moisture to assure that maximum yield and quality are achieved. On California’s central coast, where the majority of cool season vegetables are produced in the US, long-term overpumping of irrigation water has reduced groundwater levels and led to environmental degradation. Two evapotranspiration (ET) based irrigation field trials were performed near Salinas CA (USA) to determine if ET-based irrigation scheduling could conserve water while producing romaine lettuce (cv. Sun Valley) of commercially viable yield. Sprinklers were used for seed germination and crop establishment. Four drip irrigation treatments were then imposed using a randomized complete block design with six replications. The CropManage decision support model was used to estimate the full (100%) crop water requirement based mainly on ET replacement. Other treatments included 50% 75% and 150% of the full water requirement. The 100% treatment received 185 mm of water in 2015 and 247 mm in 2016, both of which were well below prior guidance and grower reports. Yields from the 100% and 150% treatments were not significantly different and were similar to industry average, while yields were significantly lower for the 50% and 75% treatments. The 100% treatment had the highest water use efficiency, and the 100% and 150% treatments together had the highest nitrogen recovery efficiency. Irrigation of romaine near the 100% ET replacement level can potentially reduce environmental impacts associated with nitrate leaching and surface runoff.
Global Nitrogen Needs to Improve Wheat Yield Under Climate Change
Increasing global food demand will require more food production without further exceeding the planetary boundaries, while at the same time adapting to climate change. We used an ensemble of wheat simulation models, with improved yield traits from the highest-yielding wheat genotypes to quantify potential yield gains and associated N requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The improved yield traits increased yield by 16% with current N fertilizer applications under both current climate and mid-century climate change scenarios. To achieve the full yield potential, a 52% increase in global average yield under a mid-century high warming climate scenario (RCP8.5), fertilizer use would need to increase fourfold over current use, which would unavoidably lead to higher environmental impacts from wheat production. Our results show the need to improve soil N availability and N use efficiency, along with yield potential.