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Record W7055292024

Climatic Effects and Farming Performance: An Overview of Selected Studies

2023· article· en· W7055292024 on OpenAlexaboutno aff

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2023
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureProductivityAgricultural productivityFood securityMicrodata (statistics)Production (economics)Population
DOInot available

Abstract

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The connection between farm productivity and climatic effects is of growing importance around the globe, as farmers are expected to satisfy a rising demand for food and agricultural products driven by an increasing population and income while contending with mounting uncertainty imposed by climate change. This presentation is a component of a larger project which seeks to establish the connection between the productivity performance of farming units and climatic effects. We seek to shed light on two specific issues: (i) what variables are most commonly-used to capture climatic effects; and (ii) to what extent does the choice of climatic indicators in production models affect the agricultural productivity measures obtained across different types of farming systems. An a priori requirement imposed when searching the literature and selecting the papers included in the analysis is that they apply stochastic production frontier (SPF) methods. The advantages and popularity of this methodology in agricultural productivity studies and beyond is well established (Fried et al., 2008, O’Donnell; 2018). The agricultural productivity literature has seen considerable growth in recent years, motivated by significant methodological developments and the increasing availability of microdata sets in some regions (e.g., LSMS-ISA data for Africa). An increasing body of productivity research is being devoted to the connection between farm output, food security and climatic effects, as well as to the role of different farming technologies or practices that can serve in strategies to promote adaptation. Two clear examples are the adoption of irrigation and improved seed varieties. The specific focus here is on three subsets of studies found in the received literature: (1) Dairy productivity studies published using data from different countries; 2) Water, irrigation, and precipitation studies again using data from different countries; and 3) Total Factor Productivity (TFP) studies that explicitly account for the climatic component in TFP in Latin America (LA) as well as in other geographical areas. Taken together, these studies provide a useful point of departure for our future work. Our choice of papers at this point is somewhat arbitrary but serves as an initial step towards undertaking a systematic search of the literature to cover a more comprehensive set of studies. We justify our current focus by noting the importance of dairy in farming systems in both the developed and developing world (Bravo-Ureta, Wall, and Neubauer 2022) and the critical role water plays in the adaptation of farming to climate change (Bopp et al. 2022). 1. Dairy Productivity Studies The measurement of TFP in dairy farming and its decomposition into different elements (e.g., technical efficiency, allocative efficiency, scale effects and technical change) has been the subject of several stochastic frontier studies going back at least to Ahmed and Bravo-Ureta (1995). Parametric output distance functions have been used to measure and decompose productivity in dairy farming by Brümmer et al. (2002) for Dutch, German and Polish farms, Newman and Matthews (2006) for Irish farms and Emvalomatis (2012) for German farms. All these studies report that TFP growth has been driven fundamentally by technological progress. Cechura et al. (2017) analyze the impact of technological progress in a study of 24 EU Member States. Aside from technical change (and efficiency gains) as drivers of TFP growth, Parikoglou et al. (2022) found that extension services contributed to the productivity growth-of Irish farms. Parametric input distance functions have been used to study dairy farm productivity by Sipiläinen et al. (2014), who investigated the profitability and productivity dynamics of Finnish and Norwegian farms; Sauer and Latacz-Lohmann (2015), who analyzed TFP change for German farms (with a Luenberger index); and Singbo and Larue (2016) for farms from Quebec. The climatic effect has been clearly absent in much of this work. 2. Water, Irrigation and Precipitation Studies Water is critical in the adaptation of farming to climate change. Therefore, we review studies that consider precipitation as a climatic variable in their models. Bravo-Ureta et al. (2016) identify 110 water studies in a meta-analysis of technical efficiency in agriculture and find that most ignore climatic effects. Among the studies that use SPF methods, only five considered a precipitation variable. McGuckin et al. (1992) specify a continuous rainfall variable in a study analyzing maize farmers in the USA. Sherlund et al. (2002) use the number of rainy days and the quantity of rain in a study of rice producers in Ivory Coast. Mariano et al. (2010) define dummies for dry and wet seasons in a rice farming study in the Philippines. Hussain et al. (2012) employ a composite variable for the number of irrigations and rainfall in a sample of wheat farmers in Pakistan. Ndlovu et al. (2014) incorporated a location dummy for high rainfall areas of maize farming in Zimbabwe. We complement these studies with 11 papers that have been published more recently. 3. Total Factor Productivity (TFP) Studies with a Climatic Component. We focus explicitly on work that examines the climatic component in TFP in LA as well as in other geographical areas. Agriculture is a major sector in the economy of most LA countries. To implement effective policies addressing climate change and promoting the adaptation of farming to the rising climatic threat, it is critical to have a thorough understanding of what drives productivity change and the role climatic effects have in the region’s agricultural productivity growth. However, a recent review by Bravo-Ureta (2021) reveals that productivity research for LA is limited and based primarily on aggregate county-level data with scant inclusion of climatic effects. We conjecture that the limited supply of studies for LA is likely due to data limitations and lack of funding to conduct the necessary work. We highlight around 10 recent articles that address the connection between climatic effects and TFP and explicitly quantify the effect of a climatic component in TFP growth.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0130.028
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.082
GPT teacher head0.290
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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