yield

In 2013 a series of meetings was held across the US with each of the Sun Grant Regional Feedstock Partnership crop teams and the resource assessment team, led by the Oregon State University and Oak Ridge National Laboratory, to review, standardize, and verify energy crop yield trials from 2007-2012 and assimilate their outcomes into a national model of biomass yield suitability. The meetings provided a way to “ground truth” yield estimates in order to accurately capture interactions of climate and soils for dedicated energy crops, including energycane, upland and lowland switchgrass, biomass sorghum, CRP grasses, hybrid poplar, willow, pine, and miscanthus x giganteus (in 2014). The verification of yield data included generating a standardized set of management assumptions for each crop and summarizing site potential yield according to the agreed cultural practices to establish, manage, and harvest each crop. From these sets of funded trials and historical data, yield was estimated across spatial gradients according to soil characteristics and climate history at a 2-week interval. The resulting national grids provide critical information for policymakers and planners of the potential productivity of these pre-commercial crops. This document summarizes the crop model and county-level results from the mapping activities (draft of document, July 31, 2014)

This document provides presentation style maps of potential crop yield of dedicated bioenergy crops from the publication "Productivity Potential of Bioenergy Crops from the Sun Grant Regional Feedstock Partnership." 2013. Eaton, Laurence, Chris Daly, Mike Halbleib, Vance Owens, Bryce Stokes. ORNL/TM-2013/574.

Abstract:
In 2013 a series of meetings was held across the US with each of the crop teams and the resource assessment team, led by the Oregon State University and Oak Ridge National Laboratory, to review, standardize, and verify yield trials from 2007-2012 crop years and assimilate their outcomes into a national model of biomass yield suitability. The meetings provided a way to “ground truth” yield estimates in order to accurately capture interactions of climate and soils for dedicated energy crops, including switchgrass, energycane, biomass sorghum, CRP grasses, miscanthus x giganteus, hybrid poplar, willow, and pine. The verification of yield data included generating a standardized set of management assumptions for each crop and summarizing site potential yield according to the agreed cultural practices to establish, manage, and harvest each crop. From these sets of funded trials and historical data, yield was estimated across spatial gradients according to soil characteristics and climate history at a 2-week interval. The resulting national grids provide critical information for policymakers and planners of the potential productivity of these pre-commercial crops.

Switchgrass (Panicum virgatum L.) is a perennial grass native to the United States that has been studied as a sustainable source of biomass fuel. Although many field-scale studies have examined the potential of this grass as a bioenergy crop, these studies have not been integrated. In this study, we present an empirical model for switchgrass yield and use this model to predict yield for the conterminous United States. We added environmental covariates to assembled yield data from field trials based on geographic location. We developed empirical models based on these data. The resulting empirical models, which account for spatial autocorrelation in the field data, provide the ability to estimate yield from factors associated with climate, soils, and management for both lowland and upland varieties of switchgrass. Yields of both ecotypes showed quadratic responses to temperature, increased with precipitation and minimum winter temperature, and decreased with stand age. Only the upland ecotype showed a positive response to our index of soil wetness and only the lowland ecotype showed a positive response to fertilizer. We view this empirical modeling effort, not as an alternative to mechanistic plant-growth modeling, but rather as a first step in the process of functional validation that will compare patterns produced by the models with those found in data. For the upland variety, the correlation between measured yields and yields predicted by empirical models was 0.62 for the training subset and 0.58 for the test subset. For the lowland variety, the correlation was 0.46 for the training subset and 0.19 for the test subset. Because considerable variation in yield remains unexplained, it will be important in the future to characterize spatial and local sources of uncertainty associated with empirical yield estimates.

This database contains current and historical official USDA data on production, supply and distribution of agricultural commodities for the United States and key producing and consuming countries.

FAOSTAT provides time-series and cross sectional data relating to food and agriculture for some 200 countries.

The national version of FAOSTAT, CountrySTAT, is being developed and implemented in a number of target countries, primarily in sub-saharan Africa. It will offer a two-way data exchange facility between countries and FAO as well as a facility to store data at the national and sub-national levels.

For several years the Idaho National Laboratory (INL) has been developing a Decision Support System for Agriculture (DSS4Ag) which determines the economically optimum recipe of various fertilizers to apply at each site in a field to produce a crop, based on the existing soil fertility at each site, as well as historic production information and current prices of fertilizers and the forecast market price of the crop at harvest. In support of the growing interest in agricultural crop residues as a bioenergy feedstock, we have extended the capability of the DSS4Ag to develop a variable-rate fertilizer recipe for the simultaneous economically optimum production of both grain and straw. In this paper we report the results of 2 yr of field research testing and enhancing the DSS4Ag?s ability to economically optimize the fertilization for the simultaneous production of both grain and its straw, where the straw is an agricultural crop residue that can be used as a biofeedstock. For both years, the DSS4Ag reduced the cost and amount of fertilizers used and increased grower profit, while reducing the biomass produced. The DSS4Ag results show that when a biorefinery infrastructure is in place and growers have a strong market for their straw it is not economically advantageous to increase fertilization in order to try to produce more straw. This suggests that other solutions, such as single-pass selective harvest, must be implemented to meet national goals for the amount of biomass that will be available for collection and use for bioenergy.