Recipient Organization
UNIV OF MINNESOTA
(N/A)
ST PAUL,MN 55108
Performing Department
(N/A)
Non Technical Summary
The long-term productivity performance of the agricultural sector is fundamentally tied to its physical location. However, the footprint of agriculture shifts over time, especially given changes in climate, soil, biotic threats, input access and other infrastructural and market attributes. Given these spatial dimensions play out over comparatively long periods of time, they demand commensurately long and consistently measured input, output, and productivity indicators to better understand these complex production processes. This project builds on the existing InSTePP Productivity Accounts, currently covering the period 1949-2012, with four overarching objectives:1. Improve and extend (backward and forward in time) sub-national agricultural input, output and productivity metrics to create data series that span upwards of a century;2.Quantify and investigate the productivity implications stemming from long-term shifts in the footprints of US agricultural production;3.Utilize these new measures alongside climate- and pest-risk metrics to deepen our understanding of the historic agricultural climate-productivity nexus; and4. Develop and deploy new methods to enhance data accessibility and sustain future updates of the core productivity series.Decisions regarding crop selection, location, and timing are pivotal for climate adaptation in farming, and are further influenced by factors such as research and development, policy, and logistics. The new data and associated analyses proposed for this project will better elucidate these critical climate-productivity relationships in the context of agricultural movement. This in turn has direct implications for a host of public and private decisions designed to deal with changing weather and climate realities that lie at the heart of production agriculture.
Animal Health Component
40%
Research Effort Categories
Basic
35%
Applied
40%
Developmental
25%
Goals / Objectives
Our overarching goals in this project are to 1) improve and extend (backward and forward in time) sub-national agricultural input, output and productivity metrics to create a data series that span upwards of a century of US agriculture; 2) quantify and investigate the productivity implications stemming from long-term shifts in the footprint of US agricultural production at multiple spatial scales (that is county, state, and national levels); 3) utilize these new measures alongside spatially congruent climate- and pest-risk metrics to deepen our understanding of the long-term climate-productivity nexus; and 4) develop and deploy new methods to enhance data accessibility and sustain future updates of the core productivity series.To achieve these broad goals, we set the following objectives:Objective 1: Revise (where possible and appropriate) and update the current University ofMinnesota International Science and Technology Practice and Policy (InSTePP) Center's inputand output series for US agriculture at the national and state levels.Objective 2: Develop a set of partial- and multi-factor productivity measures for USagriculture at the national and state levels using a range of alternative indexing procedures.Objective 3: Develop crop-specific partial-factor productivity measures for key US field cropsat the national-, state-, and county-levels, in addition to a spatially standardized gridded seriesof estimates at the sub-county level using a newly developed hierarchical gridding systemdeveloped by the University of Minnesota's GEMS Informatics Center.Objective 4: Drawing on this range of newly compiled productivity metrics, in conjunctionwith compatible gridded climate and pest-risk data developed by the GEMS InformaticsCenter, quantitatively investigate the evolving climate- pest-risk cum productivity nexus in USagriculture, paying particular attention to the productivity implications of geographical movements in thelocation of crop and agriculture production over the long term.Objective 5: Develop coded, back-end data processing workstreams coupled with anApplication Programming Interface (API) front-end to enable more sustainable, timely, andmore frequently updated access in future years to these primary productivity indicators (seeObjective 2) by way of the GEMS Exchange service hosted by the GEMS Informatics Center.
Project Methods
The methods to be used by thisproject are described in relation to each of the project products.Product 1: Revise (where possible and appropriate) and update the current InSTePP input and output series for US agriculture at the national and state levels.Based on in-depth preparatory work for this proposal over the past 18 months, we anticipate being able to expand the number of input variables from 58 to 90, in large part by parsing operator labor according to gender (male and female) for each of the age-education cohorts we had included in the existing series to account for differences in labor attributes (or quality). An important new addition will be the inclusion of irrigation water usage (from both surface and ground water) as an input variable, offering new insights into the sources and sustainable nature of agricultural productivity performance in an era of emerging water scarcity. We also anticipate a notable expansion in the number of output categories to be included from 73 to 88.Product 2: Develop a set of partial and multi-factor productivity measures for US agriculture at the national and state levels using a range of alternative indexing procedures.The disaggregated price and quantity values from Product1 will be used to form aggregate output, input and productivity indexes. These data are compiled at the national- and state-levels for each of the commodity (e.g., wheat, barley, eggs, apples, etc.) components in the output series and the respective land, labor, capital, and miscellaneous input components of the input series. Each of the series will terminate in 2022 and stretch back to at least 1949, and, depending on data availability, back to the 1930s and possibly 1920s. Given limitations in the available data for the earlier years, we envisage making alternative indexes of the respective series holding the data scope consistent over time. For example, beginning in 2012 one version of the aggregate output series will stretch back in time using the maximum amount of commodity information available.In addition to the multi-factor productivity indexes, we will also construct a suite of partial factor productivity indexes that report state and national level labor, capital, land, and materials inputs productivity trends.Our measured sense of input, output and productivity trends in US agriculture is not only sensitive to the source data and the methods used to clean and, where required, impute missing values, it is also sensitive to the choice of indexing procedure used to aggregate the primary price and quantity data. We will investigate the practical and empirical implications of alternative aggregation procedures in the context of the data and the productivity measurement purposes of the proposal.Product3: Develop crop-specific partial-factor productivity measures for key US field crops at the national-, state-, and county-levels, in addition to a spatially standardized gridded series of estimates at the sub-county level using a newly developed hierarchical gridding system developed by the University of Minnesota's GEMS Informatics Center.The lowest spatial unit for the primary data used to form the aggregate input, output and productivity metrics in Product1 is a US state. But there is much to be learned about the trends in and drivers of the productivity performance of US agriculture from a consideration of less aggregated data, for example down to the spatial level of counties and disaggregated into individual commodities. To that end we will draw on the primary data sources identified in Product1 to develop a panel data set of harvested area, yield and output using county-level data for corn, soybean, wheat, barley, and oats. These five crops accounted for 63.1% of the total harvested cropland area in the US in 2022, and 59.4% in 1930 (USDA-NASS 2023).Product 4: Drawing on this range of newly compiled productivity metrics, in conjunction with compatible, spatially gridded climate and pest risk data developed by the GEMS Informatics Center, quantitatively investigate the evolving climate-pest risk-productivity nexus in US agriculture, paying particular attention to the productivity implications of movements in the location of crop and agriculture production over the long term.This study will involve a multi-faceted, multi-scale look at the US agricultural productivity-climate-pest risk nexus over the longer term. Using our revised and extended state-level aggregate input, output, and multi-factor productivity measures, we will first revisit the work done by Acquaye et al. (2002) using an earlier version of the InSTePP Productivity Accounts that spanned the period 1949-1991. Like this earlier study, using these new Productivity Accounts we will characterize and interpret changes in the location and spatial specialization of agricultural production, but for a much longer, and up-to-date period of time. In comparing the InSTePP series with the counterpart USDA-ERS series (specially Ball et al. 1999), Acquaye et al. (2002) noted that the muted differences in productivity growth rates between the two series at the national level masked marked differences at the state level. Annual state-level productivity growth rates had positive and negative differences between the two series of up to 40%, with little in the way of systematic patterns. With the latest available USDA-ERS state-level productivity series terminating in 2004, our updated (and backfilled) state-level series will open up new prospects for a host of analytical applications.When examining the productivity implications of changes in climate (or its shorter-term manifestation weather), we will take an explicitly spatial approach, disentangling the in-situ changes in these variables from changes that are induced by shifts in the location of production. In so doing, we will factor in location-dependent, year-to-year variation in planting dates to better align time-stamped and path dependent temperature and rainfall events to the biology of a crop (see, e.g., Beddow et al. 2014).In addition to identifying the impact of crop movement on output growth for five principal US crops accounting for around 60% of total US cropland area--thus expanding on the work of Beddow and Pardey (2015) which focused only on corn--, our new data will enable us to test the nature and robustness of the results reported by Ortiz-Bobea et al. (2018) using data for the 1960-2009 period who concluded that "...[US] agriculture is growing more sensitive to climate in Midwestern states for two distinct but compounding reasons: a rising climatic sensitivity of non-irrigated cereal and oilseed crops and a growing specialization in crop production." Our assessment will use state-level productivity estimates that will span a much-extended period (1930s to 2022) and use temporarily variable, sub-state agricultural (not just cropland) area masks to develop the corresponding climate variables, thus explicitly factoring in the effects of the changing location of agriculture into our examination of the climate-productivity relationship.Product 5: Develop coded back-end data processing workstreams coupled with an Application Programming Interface (API) front-end to enable more sustainable, timelier, and more frequently updated access in future years to these primary productivity indicators (see Product2) by way of the GEMS Exchange service hosted by the GEMS Informatics Center.GEMS Informatics is building out a GEMS Exchange, whereby key agricultural datasets are made available for third-party use, accessed by way of a GEMS application programming interface (API). This project will facilitate the development of a PostgreSQL database of the state and national level input, output and productivity series that can be accessed on a subscription basis via the GEMS API. We will also develop R and Python endpoint wrappers to facilitate ready access to these data.