A more precise and quantitative method of specifying the morphological development of alfalfa (Medicago sativa L.) is needed for communication among scientists working with the crop and for associating alfalfa phenology and forage quality. A 10‐stage numerical system for individual stems (Stages to 9) was defined, and two procedures for calculating the mean stage of a herbage sample were compared on alfalfa samples grown in the spring, summer, or autumn and representing weekly age increments up to 10 weeks from all seasons. Samples were collected during one growing season from a 3‐year‐old stand of ‘Iroquois’ alfalfa grown in Tompkins County, N.Y. Mean stage by count (MSC) was the average of the individual stages present in the herbage sample, weighted for number of stems in each stage. Mean stage by weight (MSW) was the average of individual stages present, weighted for dry weight of herbage in each stage. Both methods quantified morphological development of alfalfa canopies with the anticipated effects of seasonal temperature, but the MSW procedure was superior in showing greater numerical differentiation and in distinguishing samples of diverse age and stage structure.Linear regression in vitro true digestibility (IVTD) on MSW across seasons and ages of herbage samples was highly significant:IVTD = 92.93−3.98·(MSW), r2 = 0.984, n = 11. Crude protein (CP) was characterized by a highly significant quadratic regression: CP = 40.89−7.38·(MSW) + 0.57 (MSW)2, R2 = 0.995, n = 11. The association of alfalfa quality and MSW has not been sufficiently tested to represent a general prediction model. However, the observed relationship indicates the potential usefulness of MSW in developing such a model.
dictive accuracy of a model, even when such is the researchers' explicit objective. This confusion persists. For The appropriateness of a statistical analysis for evaluating a model instance, see the 10 papers from a symposium on "Crop depends on the model's purpose. A common purpose for models in agricultural research and environmental management is accurate Modeling and Genomics" published recently in this jourprediction. In this context, correlation and linear regression are fre-nal (Agronomy Journal 95:4-113). That symposium ilquently used to test or compare models, including tests of intercept lustrates the frequent use of correlation and regression a ϭ 0 and slope b ϭ 1, but unfortunately such results are related only for model evaluation. obliquely to the specific matter of predictive success. The mean However, Kobayashi and Salam (2000) present cosquared deviation (MSD) between model predictions X and measured gent reasons why the correlation coefficient and linear values Y has been proposed as a directly relevant measure of predictive regression are not entirely satisfactory for model evaluasuccess, with MSD partitioned into three components to gain further tion and suggest that MSD and its components are often insight into model performance. This paper proposes a different and more informative. Further developing those findings, a better partitioning of MSD: squared bias (SB), nonunity slope (NU), different partitioning of MSD components has the adand lack of correlation (LC). These MSD components are distinct and additive, they have straightforward geometric and analysis of vantage of yielding distinct components with straightvariance (ANOVA) interpretations, and they relate transparently to forward meanings. regression parameters. Our MSD components are illustrated using several models for wheat (Triticum aestivum L.) yield. The MSD statistic and its components nicely complement correlation and linear COMPONENTS OF regression in evaluating the predictive accuracy of models. MEAN SQUARED DEVIATIONModel-based and measured values, X and Y, can be compared for the purpose of evaluating a simulation 1442
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.
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 agriculture's 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 state's 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.
Strategies for environmental sustainability and global food security must account for dietary change. Using a biophysical simulation model we calculated human carrying capacity under ten diet scenarios. The scenarios included two reference diets based on actual consumption and eight "Healthy Diet" scenarios that complied with nutritional recommendations but varied in the level of meat content. We considered the U.S. agricultural land base and accounted for losses, processing conversions, livestock feed needs, suitability of land for crops or grazing, and land productivity. Annual per capita land requirements ranged from 0.13 to 1.08 ha person -1 year -1 across the ten diet scenarios. Carrying capacity varied from 402 to 807 million persons; 1.3 to 2.6 times the 2010 U.S. population. Carrying capacity was generally higher for scenarios with less meat and highest for the lacto-vegetarian diet. However, the carrying capacity of the vegan diet was lower than two of the healthy omnivore diet scenarios. Sensitivity analysis showed that carrying capacity estimates were highly influenced by starting assumptions about the proportion of cropland available for cultivated cropping. Population level dietary change can contribute substantially to meeting future food needs, though ongoing agricultural research and sustainable management practices are still needed to assure sufficient production levels.
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