Current Crop Yields Research Articles

Insufficient pollinator visitation often limits yield in crop systems worldwide.

Declining pollinator populations could threaten global food production, especially if current crop yields are limited by insufficient pollinator visitation to flowers, in a phenomenon referred to as 'pollinator limitation'. Here, we assess the global prevalence of pollinator limitation, explore the risk factors, such as crop type or geographic region, that predict where pollinator limitation is more likely and ask by how much increases in pollinator visitation could improve crop yields. We address these questions using 198,360 plant-pollinator interactions and 2,083 yield measurements from 32 crop species grown in 120 study systems. We find that 28-61% of global crop systems are pollinator limited and that this limitation most frequently occurs in blueberry, coffee and apple crops. For a few datasets, we note that the probability of pollinator limitation decreases with greater forest land cover surrounding a crop field at 1 km, although average effect sizes are small. Finally, we estimate that for those crops we identify as pollinator limited, increasing pollinator visitation at all farms to existing levels observed in the 90th percentile of each study system would close 63% of yield gaps between high- and low-yielding fields. Our findings show variations in sensitivity to pollinator limitation across diverse crop systems and indicate that realistic increases in pollinator visitation could mitigate crop yield shortfalls attributable to pollinator limitation.

A multi-objective optimization approach to simultaneously halve water consumption, CH4, and N2O emissions while maintaining rice yield

Sustainable rice farming practices are urgently needed to meet increasing food demand, cope with water scarcity, and mitigate climate change. Traditional farming methods that prioritize a single objective, such as increasing crop yield, reducing irrigation water consumption, or minimizing greenhouse gas (GHG) emissions, have proven to be insufficient. Simultaneously optimizing multiple competing rice farming objectives remains less explored. The present study aimed to increase current rice yield, reduce irrigation water consumption, and tackle the dilemma to reduce GHG (CH4 and N2O) emissions at once. Using a heuristic and holistic method, we optimized farm management, focusing on irrigation regimes, sowing window, fertilization rate, tillage depth, and their interactions. We calibrated and validated the process-based DeNitrification–DeComposition (DNDC) model with 5 years of eddy covariance observations in a rice paddy site. The DNDC model was integrated with a Pareto-based non-dominated sorting genetic algorithm (NSGA-III) to solve the multi-objective optimization problem. Results show that current farm management practices have failed to achieve the potential yield and imposed high environmental costs in the form of water use (604–810 mm/yr) and GHG emissions (CH4: 186–220 kg C/ha/yr; N2O: 0.3–1.6 kg C/ha/yr). By contrast, our new approach suggested that the optimized management could maintain or even increase current crop yield to its potential (∼10 t/ha), while reducing irrigation demand and GHG emissions by >50 %. Our results suggest that earlier sowing, combined with improved irrigation practices, may play a crucial role in maximizing crop yield and potentially providing environmental benefits. We found that the optimal proportion of non-flooded days was around 54 % of the growing season length, and the optimal stage for non-flooding days was the vegetative stage. Our approach could further be applied to evaluate the environmental sustainability of farming systems under various climate and local conditions, and to guide policymaking and farming practices.

Evaluating the impact of a 2.5–3°C increase in temperature on drought-stressed German wheat cultivars under natural stress conditions

Climate change in Central Europe is expected to influence crop growth through frequent drought and heat extremes. Wheat contributing about 20% of the total dietary calories and proteins worldwide, is sensitive to heat and drought, especially during its reproductive stages. To develop strategies to improve climate resilience in wheat, a comprehensive field evaluation was carried out under drier and warmer field conditions in Moldova from 2016 to 2019 for forty winter wheat varieties from Germany and Eastern Europe. The grain yield of German cultivars grown in Moldova ranged from 4.46–6.58 t/ha from 2016 to 2019 and was 40% lower compared to the same cultivars grown in Southern Germany. This is significantly more than the reduction predicted by current process-based crop yield models considering only seasonal temperature effects and not combined drought effects. Among all the wheat varieties tested in Moldova, Eastern European varieties and German hybrids achieved the best yield performance across the years. The lower grain yield of German wheat varieties was mainly due to the reduction in grain filling duration, which led to a decrease in the grain filling rate and thousand-grain weight. A higher grain filling rate contributed to a higher grain yield in the Eastern European varieties. These results suggest that well-adapted varieties with a faster rate of development and an earlier start of grain filling have a notable advantage under drought and heat conditions, providing a useful genetic source to increase heat and drought tolerance in future plant breeding programs. The space-for-time approach used in this study allows for the investigation of combined heat and drought, reflecting anticipated or future climate conditions without taking into account CO2 effects.

Mobility and Degradation of Pesticides in Soil-Risk to Groundwater Contamination

Pesticides are used in modern agriculture to prevent crop losses by different pests and allow sustentation of the current crop yields. The pesticides after their use enter the environment viz., soil, water and air, then undergo various interactions and complex transformations resulting in the formation of multiple metabolites. The fate of pesticides in the environment depends on the properties of pesticide, soil and some other miscellaneous factors. Pesticides and their metabolites may move downward and reach groundwater under the effect of gravity, percolation, agricultural runoff or backsiphoning, hence there is risk to groundwater contamination. Chemicals have been noticed in groundwater, drinking water and surface water supplies. Therefore, research on ill-effects of the pesticide use and their fate in the environment is of great concern. The indigenous micro-flora is successful in biodegradation and removal of xenobiotics. So, in order to ensure public safety as well as the environmental protection from the chemical use, the pesticide use must be judicious and biodegradation of such chemicals to reduce their residues in agricultural products needs to be escalated.

Cassava: Farmers Adoption and Livelihood in Bono Region Performance of Stochastic Frontier Functions

The objective is to analyze the stochastic frontier function in comparing the performance of the Cobb-Douglas and Translog Frontier functions in the cultivation of cassava in the Bono Region of Ghana. The assumption is that given the same level of productive inputs of farmers at any given farming season, across heterogeneous farmlands, both functions are likely to produce the same results. The interview was used as the instrument for obtaining plot-specific data from 120 cassava farmers across six districts and data was analyzed using the quantitative technique. Direct predictors of output include plot size, labor, hoes, cutlasses, and cassava stems. Using a half-normal distributional assumption, the study evaluates variance parameters of the composed error terms. The results showed that the estimated functions produced comparable results in terms of magnitude and signs of input variables. While efficiency appeared to be much higher in Cobb-Douglas than in the Translog function, the variance parameter score for the CD function is significantly different from those of the Translog function, and the maximum output attainable for the given productive inputs was 40% and 15% respectively. This means that farmers can scale up their current crop yield by 60% and 85% respectively of their frontier functions using the same inputs and technology if the appropriate interventions are carried out. The limitation of the study is the non-inclusion of environmental factors such as rain as productive input and the study is limited to comparing frontier functions. The results underscored the importance of examining the current production behavior of farmers for reliability and policy inferences

Predictive metabolomics of multiple Atacama plant species unveils a core set of generic metabolites for extreme climate resilience.

Summary Current crop yield of the best ideotypes is stagnating and threatened by climate change. In this scenario, understanding wild plant adaptations in extreme ecosystems offers an opportunity to learn about new mechanisms for resilience. Previous studies have shown species specificity for metabolites involved in plant adaptation to harsh environments.Here, we combined multispecies ecological metabolomics and machine learning‐based generalized linear model predictions to link the metabolome to the plant environment in a set of 24 species belonging to 14 families growing along an altitudinal gradient in the Atacama Desert.Thirty‐nine common compounds predicted the plant environment with 79% accuracy, thus establishing the plant metabolome as an excellent integrative predictor of environmental fluctuations. These metabolites were independent of the species and validated both statistically and biologically using an independent dataset from a different sampling year. Thereafter, using multiblock predictive regressions, metabolites were linked to climatic and edaphic stressors such as freezing temperature, water deficit and high solar irradiance.These findings indicate that plants from different evolutionary trajectories use a generic metabolic toolkit to face extreme environments. These core metabolites, also present in agronomic species, provide a unique metabolic goldmine for improving crop performances under abiotic pressure.

Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System

Many approaches for crop yield prediction were analyzed by countries using remote sensing data, but the information obtained was less successful due to insufficient data gathered due to climatic variables and poor image resolution. As a result, current crop yield estimation methods are obsolete and no longer useful. Several attempts have been made to overcome these difficulties by combining high precision remote sensing images. Furthermore, such remote sensing-based working models are better suited to extraterrestrial farmers and homogeneous agricultural areas. The development of this innovative framework was prompted by a scarcity of high-quality satellite imagery. This intelligent strategy is based on a new theoretical framework that employs the energy equation to improve crop yield predictions. This method was used to collect input from multiple farmers in order to validate the observation. The proposed technique’s excellent reliability on crop yield prediction is compared and contrasted between crop yield prediction and actual production in different areas, and meaningful observations are provided.

Over-expression of TaDWF4 increases wheat productivity under low and sufficient nitrogen through enhanced carbon assimilation

There is a strong pressure to reduce nitrogen (N) fertilizer inputs while maintaining or increasing current cereal crop yields. We show that overexpression of TaDWF4-B, the dominant shoot expressed homoeologue of OsDWF4, in wheat can increase plant productivity by up to 105% under a range of N levels on marginal soils, resulting in increased N use efficiency (NUE). We show that a two to four-fold increase in TaDWF4 transcript levels enhances the responsiveness of genes regulated by N. The productivity increases seen were primarily due to the maintenance of photosystem II operating efficiency and carbon assimilation in plants when grown under limiting N conditions and not an overall increase in photosynthesis capacity. The increased biomass production and yield per plant in TaDWF4 OE lines could be linked to modified carbon partitioning and changes in expression pattern of the growth regulator Target Of Rapamycin, offering a route towards breeding for sustained yield and lower N inputs.

The Use of Calculus to Determine Efficient Fertilizer Levels for Crop Production

For this project, I wanted to incorporate calculus into agriculture and environmental science methods. More in detail, the problem used asked for the maximum levels of nitrogen (N) and phosphorus (P) that would be best for a current crop yield. This allowed incorporating partial derivatives, and critical points to find the maximum values for the equation. The results show that in order to demonstrate maximum crop yield production, the levels of nitrogen (N) and phosphorus (P) were to be both at 2, with the correct corresponding units. The drawback from this problem is that although the problem showed effective nitrogen and phosphorus levels, it should be determined as to whether those levels, typically in synthetic fertilizers, are more, less, or equal to the suggested amount per bushel. Adding excess nutrients onto a crop can result in nutrient in our waterways due to surface water runoff. The overall purpose would be a moral question for the farmer: Does the farmer add the levels of N and P for the maximum crop yield success, regardless of it being more than the suggested bushel amount?

Future Crop Yield Projections Using a Multi-model Set of Regional Climate Models and a Plausible Adaptation Practice in the Southeast United States

Since maize, peanut, and cotton are economically valuable crops in the southeast United States, their yield amount changes in a future climate are attention-grabbing statistics demanded by associated stakeholders and policymakers. The Crop System Modeling—Decision Support System for Agrotechnology Transfer (CSM-DSSAT) models of maize, peanut, and cotton are, respectively, driven by the North American Regional Climate Change Assessment Program (NARCCAP) Phase II regional climate models to estimate current (1971–2000) and future (2041–2070) crop yield amounts. In particular, the future weather/climate data are based on the Special Report on Emission Scenarios (SRES) A2 emissions scenario. The NARCCAP realizations show on average that there will be large temperature increases (~2.7 °C) and minor rainfall decreases (~−0.10 mm/day) with pattern shifts in the southeast United States. With these future climate projections, the overall future crop yield amounts appear to be reduced under rainfed conditions. A better estimate of future crop yield amounts might be achievable by utilizing the so-called weighted ensemble method. It is proposed that the reduced crop yield amounts in the future could be mitigated by altering the currently adopted local planting dates without any irrigation support.

An estimate of the potential economic impacts of climate change on Egypt’s agriculture: a multi-market model approach

ABSTRACT Quantifying the economic effects of climate change is a crucial step for planning adaptation in developing countries. For Egypt, global warming has put it among the most vulnerable countries. Wherefore, this study assesses the economy-wide impacts of climate change on Egyptian agriculture using multimarket model. The results show that in the worst-case scenario (Lower yields and higher water demand), the agricultural production will decrease by a range of 10 to 18% for commodity. Besides, more agricultural labors could lose their jobs. It is also found that there will be a rise in both consumer prices (by 7% to 24%) and producer prices (by 12% to 22%) for all commodities. Furthermore, value-added and agricultural income will fall by 29% and 12% respectively, compared to baseline. In conclusion, it is necessary to put in place measures and effort to address these potential risks in the next few years. Accordingly, the study recommends the following: Firstly, efficient use of water resources to reduce the demand for irrigation water. Secondly, more drought, heat, and salinity adaptive varieties are required. The development of these varieties is critical to increasing productivity under more high-temperature conditions or to the maintenance of the same levels of current crop yields.

Spatial and Temporal Trends in the Yields of Three Major Crops: Wheat, Rice and Maize in India

India is one of the largest landmasses under crop cultivation with staple consumption of food security crops. Post green revolution (since 1967), there has been a steady March towards self-sustainability in food production. Though the general trends speak of an increase in crop yields, the detailed study on decade wise trends and fast approaching state of stagnation in crop yields is largely missing. The present manuscript is the first study examining the crop yield trends of three major food crops—wheat, rice, and maize across Indian states. The four types of regression models were fitted on the annual yield data from all the 29 Indian states over the period 1967–2017. The best-fit statistical models were chosen using the Akaike information criterion. Our results suggest that (1) Wheat yields in 13 Indian states, rice yields in eleven Indian states, and maize yields in six Indian states are now not improving. (2) The yields in ~ 76% of wheat harvested area (~ 18.5 million hectares), ~ 47% of rice harvested area (~ 19.5 million hectares), and ~ 18% of maize harvested area (~ 1.2 million hectares) are not improving for the recent decade. The detailed mapping of current crop yield trends across Indian states is the first step towards achieving a bigger goal of identifying the responsible factors affecting current crop yield trends and then identifying and recommending appropriate mitigation strategies.

Take out the farmer: An economic assessment of land expropriation for urban expansion in Bahir Dar, Northwest Ethiopia

In Ethiopia, the demand for land for urbanisation is primarily met by converting rural land through expropriation. However, land expropriations are adversely affecting the previous land users by reducing the amount of production and their sources of income. In Bahir Dar, one of the fastest-growing cities in Ethiopia, approximately 300 landholdings are expropriated each year, on average, for urban expansion. This paper assesses the land expropriations to examine whether they offer economically appropriate compensation for the previous land users. Land expropriations for urbanisation between 2007/2008 and 2016/2017 were analysed based on data on land expropriation and its compensation payment obtained from the Bahir Dar City Land Administration and Management Office. Data were analysed using an exponential growth model and a stochastic budgeting technique in which Monte Carlo simulations are performed. Between 2007/2008 and 2016/2017, more than 1500 ha of land were included in the city’s boundary through expropriation from 2900 landholders. The affected farmers received compensation that represents only 37 per cent of the value of current crop yields and its growth. The current compensation scheme ignores the impact of inflation on the prices of crops and assumes constant yields. It also excludes the value of crop residuals. We propose a workable discounted compensation framework that considers crop price and yield growths. This will make the compensation scheme more appropriate and make the affected farmers better off.

Lean optimization model for managing the yield of pima cotton (gossipier Barba dense) in small- and medium-sized farms in the Peruvian coast areas

This study proposes a lean manufacturing-based optimization model to standardize “Pima cotton” crop yields in the Peruvian coast areas toward the north of Piura as the study area. The study also discusses how Pima cotton is grown in the Peruvian cost areas. This is represented in 2 stages: diagnosis and development of the cotton crop model. The diagnosis stage consists of 3 steps, and the development stage consists of 2 steps. The interrelation of each stage has been identified with the lean manufacturing principle for the improvement of crop yield. Results showed that to increase current crop yield in Piura to 172 bushels/ha as well as determine the quantities of resources and raw materials required considering a standardized crop production process. The limitations of the research depend on the climatic conditions in Peru. The main contribution of this research is to propose a model using which farmers may produce Pima cotton by utilizing the indicators proposed to increase the process control. In addition, the paper proposes a standardized crop production process that farmers must follow, supported by a mathematical model simulation.

Profiling of the Differential Abundance of Drought and Salt Stress-Responsive MicroRNAs Across Grass Crop and Genetic Model Plant Species

In recent years, it has become readily accepted among interdisciplinary agriculturalists that the current global crop yield to land capability ratio is significantly insufficient to achieve food security for the predicted population of 9.5 billion individuals by the year 2050. This issue is further compounded by the: (1) food versus biofuel debate; (2) decreasing availability of arable land; (3) required reductions to the extensive and ongoing environmental damage caused by either poor agricultural practices or agriculture expansion, and; (4) increasingly unfavorable (duration and severity) crop cultivation conditions that accompany man-made climate change, driven by ever-expanding urbanization and its associated industrial practices. Mounting studies are repeatedly highlighting the critical importance of linking genotypes to agronomically beneficial phenotypes and/or using a molecular approach to help address this global crisis, as “simply” clearing the remaining natural ecosystems of the globe for the cultivation of additional, non-modified crops is not efficient, nor is this practice sustainable. The majority of global food crop production is sourced from a small number of members of the Poaceae family of grasses, namely; maize (Zea mays L.), wheat (Triticum aestivum L.) and rice (Oryza sativa L.). It is, therefore, of significant concern that all three of these Poaceae grass species are susceptible to a range of abiotic stresses, including drought and salt stress. Highly conserved among monocotyledonous and dicotyledonous plant species, microRNAs (miRNAs) are now well-established master regulators of gene expression, influencing all aspects of plant development, mediating defense responses against pathogens and adaptation to environmental stress. Here we investigate the variation in the abundance profiles of six known abiotic stress-responsive miRNAs, following exposure to salt and drought stress across these three key Poaceae grass crop species as well as to compare these profiles to those obtained from the well-established genetic model plant species, Arabidopsis thaliana (L.) Heynh. Additionally, we outline the variables that are the most likely primary contributors to instances of differential miRNA abundance across the assessed species following drought or salt stress exposure, specifically; (1) identifying variations in the experimental conditions and/or methodology used to assess miRNA abundance, and; (2) the distribution of regulatory transcription factor binding sites within the putative promoter region of a MICRORNA (MIR) gene that encodes the highly conserved, stress-responsive miRNA. We also discuss the emerging role that non-conserved, species-specific miRNAs play in mediating a plant’s response to drought or salt stress.

Simulating Soil Organic Carbon Responses to Cropping Intensity, Tillage, and Climate Change in Pacific Northwest Dryland.

Managing dryland cropping systems to increase soil organic C (SOC) under changing climate is challenging after decades of winter wheat ( L.)-fallow and moldboard plow tillage (W-F/MP). The objective was to use CQESTR, a process-based C model, and SOC data collected in 2004, 2008, and 2012 to predict the best management to increase SOC under changing climate in four cropping systems, which included continuous wheat under no tillage (W-W/NT), wheat and sorghum × sudangrass [ (L.) Moench. × L.] under no tillage, wheat-fallow under sweep tillage, and W-F/MP. Since future yields and climate are uncertain, 20 scenarios for each cropping system were simulated with four climate projections and five crop yield scenarios (current crop yields, and 10 or 30% greater or lesser yields). Measured and simulated SOC were significantly ( < 0.0001) correlated ( = 0.98) at all soil depths. Predicted SOC changes ranged from -12.03 to 2.56 Mg C ha in the 1-m soil depth for W-F/MP and W-W/NT, respectively, during the 2012 to 2052 predictive period. Only W-W/NT sequestered SOC at a rate of 0.06 Mg C ha yr under current crop yields and climate. Under climate change and yield scenarios, W-W/NT lost SOC except with a 30% wheat yield increase for 40 yr. Predicted SOC increases in W-W/NT were 0.71, 1.16, and 0.88 Mg C ha under the Oregon Climate Assessment Reports for low emissions and high emissions and the Regional Climate Model version 3 with boundary conditions from the Third Generation Coupled Global Climate Model, respectively, with 30% yield increases. Continuous no-till cropping would increase SOC and improve soil health and resiliency to lessen the impact of extreme weather.

Multi-Dimensional Evaluation of Simulated Small-Scale Irrigation Intervention: A Case Study in Dimbasinia Watershed, Ghana

This paper studied the impacts of small-scale irrigation (SSI) interventions on environmental sustainability, agricultural production, and socio-economics using an Integrated Decision Support System (IDSS). The IDSS is comprised of a suite of models, namely the Soil and Water Assessment Tool (SWAT), Agricultural Policy/Environmental eXtender (APEX), and Farm Income and Nutrition Simulator (FARMSIM). The IDSS was applied in Dimbasinia watershed in northern Ghana using irrigation water from shallow groundwater. The watershed has a modest amount of shallow groundwater resources. However, the average annual irrigation water requirement exceeded the average annual shallow groundwater recharge. It was found that the current crop yield in Dimbasinia watershed was only ~40% of the potential crop production. This is mainly related to climate variability, low soil fertility, and land-management practices. For example, application of 50 kg/ha urea and 50 kg/ha DAP doubled maize and sorghum yield from the current farmers’ practices. Better income was obtained when irrigated vegetables/fodder were cultivated in rotation with sorghum as compared to in rotation with maize. Investment in solar pumps paid better dividends and also supplied clean energy. The socio-economic analysis indicated that having irrigated dry season vegetables will improve household nutrition. Since shallow groundwater recharge alone may not provide sufficient water for irrigation in a sustainable manner, surface water may be stored using water-harvesting structures to supplement the groundwater for irrigation. Integrated use of the water resources will also reduce depletion of the shallow groundwater aquifer. We conclude that IDSS is a promising tool to study gaps and constraints as well as upscaling of SSI.

Optimal Nutrient Concentration Ranges of ‘Hass’ Avocado Cauliflower Stage Inflorescences—Potential Diagnostic Tool to Optimize Tree Nutrient Status and Increase Yield

Optimizing ‘Hass’ avocado (Persea americana Mill.) tree nutrient status is essential for maximizing productivity. Leaf nutrient analysis is used to guide avocado fertilization to maintain tree nutrition. The goal of this research was to identify a ‘Hass’ avocado tissue with nutrient concentrations predictive of yields greater than 40 kg of fruit per tree. This threshold was specified to assist the California avocado industry to increase yields to ≈11,200 kg·ha−1. Nutrient concentrations of cauliflower stage inflorescences (CSI) collected in March proved better predictors of yield than inflorescences collected at full bloom (FBI) in April, fruit pedicels (FP) collected at five different stages of avocado tree phenology from the end of fruit set in June through April the following spring when mature fruit enter a second period of exponential growth, or 6-month-old spring flush leaves (LF) from nonbearing vegetative shoots collected in September (California avocado industry standard). For CSI tissue, concentrations of seven nutrients, nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), sulfur (S), zinc (Zn), and copper (Cu) were predictive of trees producing greater than 40 kg of fruit annually. Conditional quantile sampling and frequency analysis were used to identify optimum nutrient concentration ranges (ONCR) for each nutrient. Optimum ratios between nutrient concentrations and yields greater than 40 kg per tree were also derived. The high nutrient concentrations characterizing CSI tissue suggest current fertilization practices (timing or amounts) might be causing nutrient imbalances at this stage of avocado tree phenology that are limiting productivity, a possibility that warrants further investigation. Because CSI samples can be collected 4–6 weeks before full bloom, nutritional problems can be addressed before they affect flower retention and fruit set to increase current crop yield, fruit size, and quality. Thus, CSI nutrient analysis warrants further research as a potential supplemental or alternative tool for diagnosing ‘Hass’ avocado tree nutrient status and increasing yield.

Impact of roughage-concentrate ratio on the water footprints of beef feedlots

The aim of this study was to determine the water footprint of beef feedlots up to the farm gate and evaluate the impact of roughage-concentrate ratio on the green water footprint. The study purpose was to provide strategic insights about nutritional management and water used that have a positive impact reducing water demand and increasing water efficiency. A regional bottom-up approach of the beef feedlot production was applied and water footprint methodology was used as the primary method. We included green and blue volumetric water footprint. Sensitivity assessment was done to explore differences in agricultural performance. Total water footprint ranged from 1935 to 9673m3kg−1 of meat. The results are demonstrating the variability in water footprint that can exist from farm to farm. Green water represented on average 84.5% and blue water 15.4% of the footprint value. The farms with larger amounts of concentrate in the diet had high footprint values and the differences in feed composition have a significant effect on the water footprint. The average water footprint of the current crop yield was 5814Lkg−1 of meat. With a reduction of 25% in the current crop yields, it was 7.416Lkg−1 of meat and with an increase of 25% in the current crop yields, 4677Lkg−1 of meat. These results show that increasing agricultural productivity has positive impacts on reducing the water footprint. The results show that the water footprint values of feedlots are determined largely by the type of animal diet and by performance indicators of the animals. The roughage-concentrate ratio and type of roughage are the nutritional aspects that most significantly influence the footprint values. This study supports the recommendation that beef feedlots should place emphasis on maximizing the use of roughage, because this could decrease the pressure on fresh water resources.

Biomass supply chain management in North Carolina (part 1): predictive model for cropland conversion to biomass feedstocks

Increased interest in biomass cultivation requires detailed analysis of spatial production potential of possible biorefinery locations, with emphasis on feedstock production cost minimization. Integrated assessment of publicly available spatial data on current crop production, soil type, and yield potential, coupled with techno-economic production cost estimates, can support a functional method for rapid analysis of potential biorefinery sites. A novel predictive model was developed to determine cropland conversion using a probabilistic profit based equation for multiple biomass crops: giant reed, miscanthus, switchgrass, and sorghum (with either canola or barley as a winter crop). The three primary regions of North Carolina (Mountains, Piedmont, and Coastal Plain) were used as a case study and with a single parameter uncertainty analysis was completed. According to the model, the county chosen to represent the Coastal Plain (Duplin County) had the largest potential acreage that would be converted (15,071 ha, 7.1% total land, 9.3% of cropland) primarily to sorghum with canola as a winter crop. Large portions were also predicted to convert to giant reed and switchgrass, depending on the price and yield parameters used. The Piedmont (Granville County, 7697 ha, 5.5% total land, 6.9% cropland) and Mountain (Henderson County, 2117 ha, 2.2% total land, 2.3% cropland) regions were predicted to convert primarily to switchgrass acreage for biomass production, with much less available biomass overall compared to the Coastal Plain. This model provided meaningful insight into regional cropping systems and feedstock availability, allowing for improved business planning in designated regions. Determination of cropland conversion is imperative to develop realistic biomass logistical operations, which in conjunction can assist with rapid determination of profitable biomass availability. After this rapid analysis method is conducted in-depth on-ground biorefinery feasibility analysis can occur, ensuring resource are used only in locations with a high potential for available low cost biomass feedstocks.