Gary W. Fick

Testing a complete-diet model for estimating the land resource requirements of food consumption and agricultural carrying capacity: The New York State example

Agriculture faces a multitude of challenges in the 21st century, and new tools are needed to help determine how it should respond. Among these challenges is a need to reconcile how human food consumption patterns should change to both improve human nutrition and reduce agricultures environmental footprint. A complete-diet framework is needed for better understanding how diet influences demand for a fundamental agricultural resource, land. We tested such a model, measuring the impact of fat and meat consumption on the land requirements of food production in New York State (NYS). Analysis was confined to this geographic area to simplify the modeling procedure and to examine the states ability to reduce environmental impact by supplying food locally. Per capita land resource requirements were calculated with a spreadsheet model for 42 diets ranging from 0 to 381 g d −1 (0 to 12 oz d −1 ) of meat and eggs and 20 to 45% total calories from fat. Many of these diets meet national dietary recommendations. The potential human carrying capacity of the NYS land base was then derived, based on recent estimates of available agricultural land. A nearly fivefold difference (0.18–0.86 ha) in per capita land requirements was observed across the diets. Increasing meat in the diet increased per capita land requirements, while increasing total dietary fat increased the land requirements of low meat diets but reduced the land needed for high meat diets. Higher meat diets used a larger share of the available cropland suited only to pasture and perennial crops. Thus, only a threefold difference was observed for the potential number of people fed from the NYS land base (2.0–6.2 million). In addition, some high-fat vegetarian diets supported fewer people than lower fat diets containing 63–127 g d −1 of meat (approximately one- to two-thirds of the national average per capita consumption in the US). These results support the assertion that diet should be considered in its entirety when assessing environmental impact. To more completely understand how diet influences land requirements and potential carrying capacity, this model should be applied across a larger geographic area that encompasses a wider variety of climates and soil resources. To better understand the ability of a local region to supply more of its own food, the model should be moved into a geospatial framework.

Foodshed analysis and its relevance to sustainability

Providing a wholesome and adequate food supply is the most basic tenet of agricultural sustainability. However, sharp increases in global food prices have occurred in the past 2 years, bringing the real price of food to the highest level seen in 30 years (FAO, 2008). This dramatic shift is a fundamental concern. The role of ‘local food’ in contributing to the solution of underlying problems is currently being debated, and the debate raises a critical question: To what degree can society continue to rely on large-scale, long-distance transportation of food? Growing concerns about climate change, the longevity of fossil fuel supplies and attempts to produce energy from agriculture suggest that energy efficiency will be critical to adapting to resource constraints and mitigating climate impacts. Moreover, these problems are urgent because energy prices, biofuel production and weather-related crop failures are partially responsible for the current world food price situation. Tools are needed to determine how the environmental impact and vulnerability of the food system are related to where food is produced in relation to where it is consumed. To this end, analyses of foodsheds, the geographic areas that feed population centers, can provide useful and unique insights.

Mapping potential foodsheds in New York State: A spatial model for evaluating the capacity to localize food production

Growing interest in local food has sparked debate about the merits of attempting to reduce the distance food travels. One point of contention is the capacity of local agriculture to meet the food needs of local people. In hopes of informing this debate, this research presents a method for mapping potential foodsheds, land areas that could theoretically feed urban centers. The model was applied to New York State (NYS). Geographic information systems were used to estimate the spatial distribution of food production capacity relative to the food needs of NYS population centers. Optimization tools were then applied to allocate production potential to meet food needs in the minimum distance possible. Overall, the model showed that NYS could provide 34% of its total food needs within an average distance of just 49 km. However, the model did not allocate production potential evenly. Most NYS population centers could have the majority of their food needs sourced in-state, except for the greater New York City (NYC) area. Thus, the study presents a mixed review of the potential for local food systems to reduce the distance food travels. While small- to medium-sized cities of NYS could theoretically meet their food needs within distances two orders of magnitude smaller than the current American food system, NYC must draw on more distant food-producing resources. Nonetheless, the foodshed model provides a successful template for considering the geography of food production and food consumption simultaneously. Such a tool could be valuable for examining how cities might change their food procurement to curb greenhouse gas emissions and adapt to depletion of petroleum and other energy resources necessary for long-distance transport of food.

Mapping potential foodsheds in New York State by food group: An approach for prioritizing which foods to grow locally

Public interest in local food continues to grow, but few analyses have examined the capacity for the US population to be supplied through local and regional food systems. This paper extends earlier work that demonstrated a method for mapping potential foodsheds and estimating the potential for New York to meet the food needs of the states population centers. It provides a methodology for addressing the question, ‘If land is limited, which foods should be grown locally?’ A spatial model was developed to allocate the available agricultural land of New York State (NYS) to meet in-state food needs for six distinct food groups (grains, vegetables, fruits, dairy, meat and eggs) across the eight largest population centers. An optimization routine was used to allocate land to maximize economic land use value (LUV). Eleven scenarios were examined, ranging from a baseline level of consumption of New York produced foods to a 100% local diet. Across the 11 scenarios, the amount of food supplied, the LUV attained, and the area of land allocated increased as the ‘willingness’ to consume local products increased. This approach dictated that land was preferentially devoted to higher-value food groups relative to lower-value groups, and no scenario used all available land. Under the 100% local scenario, 69% of total food needs (on a fresh weight basis) were supplied in-state with an average food distance of 238 km. This scenario provided food from only four of the six groups, namely, dairy, eggs, fruit and vegetables. These results suggest that a much larger proportion of total food needs (on a weight basis) might be provided from in-state production than was found in previous work. LUV serves as a compelling optimization function, and future work should investigate the degree to which maximizing returns to land complements or conflicts with social and environmental goals of local and regional food systems.

Modeling optimal alfalfa harvest scheduling using short-range weather forecasts

Alfalfa (Medicago sativa L.) harvest is ideally scheduled to avoid rain damage to the cut forage while it is still in the field. The problem of timing alfalfa harvest in relation to available weather forecasts is addressed here using a dynamic programming approach. The modeled cutting decision is faced daily, and it must balance increasing yield and decreasing forage quality if cutting is deferred, with potential weather-related losses evaluated using probability forecast information for the upcoming 24-h period. Specific results for central New York conditions produce a four-cut system in most years, and indicate that alfalfa preservation as wilted silage is probably preferable to either direct-cut silage or dry hay. Using weather forecasts to schedule alfalfa cutting is estimated to increase average annual crop value by about 5–10%, while decreasing income variability.

A pasture production model for use in a whole farm simulator

Abstract Modular simulation models of crop and animal enterprises can be used in concert to simulate a particular whole farm operation and thus provide management information for individual farmers and farm advisers. This paper reports the development of a pasture module for such a whole farm simulator designed to study irrigation management in New Zealand. The model (CANPAS) describes the production of perennial ryegrass/white clover pastures under either dryland or irrigated conditions. Seasonal growth studies and the principles of ecological physiology form the theoretical base of CANPAS. Whenever possible, parts of other models were also used. With time steps of one day, CANPAS predicts the net above-ground yield of live and dead herbage, herbage digestibility, leaf area index and soil water supply. Harvested yields are also predicted for simulated grazing or cutting managements, assuming part of the above-ground yield of live and dead herbage is left as stubble. Field measurements of harvested pasture yields from the Canterbury Plains of New Zealand were used to validate the model. Validation procedure included the use of a simple set of statistical tests. The final version (CANPAS III) gave an overall r2 of 0·83, but poorer performance was found under some test managements. Discussion covers the general aspects of this kind of modelling as well as the specific details of the pasture module.

A warm-season annual grass growth model parameterized for maize and sudangrass

Abstract A process-based, warm-season annual grass growth model named WANGRO was developed to simulate the grass component of a legume-grass rotation under water limiting conditions. The model is parameterized for maize ( Zea mays L.) and sudangrass ( Sorghum bicolor (L.) Moench var. sudanense Piper) dry matter production. The processes modeled are crop emergence, canopy light absorption and shoot growth, dry matter partitioning into above-ground plant parts, evapotranspiration, and root growth. Model detail is relatively limited to facilitate the goal of model use in management. Processes are simulated with daily integration time steps. A unique feature of the model is that maize and sudangrass are simulated from planting to physiological maturity by modifying only three crop specific parameters. The input requirements of the model are weather data (temperature, rainfall and solar radiation), soil parameters, and management data. The potential of the model for general applicability to the crop growing conditions of the Northeast is indicated by good agreement of the model yield predictions with the corresponding field data. Model predicted dry matter yields were within 15, 13 and 12% of the field-observed yields for maize grain, maize silage, and sudangrass above-ground biomass, respectively.