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Record W2089032461 · doi:10.1017/s1742170510000591

Effects of input management and crop diversity on economic returns and riskiness of cropping systems in the semi-arid Canadian Prairie

2011· article· en· W2089032461 on OpenAlexaffabout
R.P. Zentner, Prakash Basnyat, S.A. Brandt, A. G. Thomas, D. Ulrich, C. A. Campbell, C.N. Nagy, B. Frick, R. Lemke, S. S. Malhi, O. Olfert, M. R. Fernandez

Bibliographic record

VenueRenewable Agriculture and Food Systems · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of SaskatchewanAgriculture and Agri-Food Canada
Fundersnot available
KeywordsTillageCrop rotationAgronomyEnvironmental scienceCroppingSummer fallowCropping systemAgroforestryLoamNo-till farmingAgricultureCropSoil waterSoil fertilityBiologyEcology

Abstract

fetched live from OpenAlex

Abstract Producers in the semi-arid Dark Brown Chernozemic (Typic Boroll) soil zone of the Canadian Prairie are contemplating changes to land-use practices, moving away from conventional high-input production systems that specialize in one or two annual grain crops to more diversified and extended cropping systems that use reduced-input and organic management practices. This study examined the economic merits of nine cropping systems, consisting of a factorial combination of three input management methods and three levels of cropping diversity. It was conducted over the 1996–2007 period on a loam soil at Scott, Saskatchewan. The input treatments were: (1) high input (HIGH), which used conventional tillage and full recommended rates of fertilizers and pesticides ‘as required’; (2) reduced input (RED), which used conservation tillage and integrated weed and nutrient management practices in an effort to lower requirements for fuel, fertilizers and pesticides; and (3) organic input (ORG), which used tillage, non-chemical pest control, higher seeding rates, delayed seeding and legume crops to replenish soil nutrients. The crop diversity treatments included: (1) a fallow-based rotation with low crop diversity (LOW); (2) a diversified annual rotation of cereal, oilseed and pulse grains (DAG); and (3) a diversified rotation using annual grains and perennial forages (DAP). All crop rotations were 6 years in length. At the 2007 input costs and prices, average net returns and 12-year net present values were higher for organic than for non-organic treatments, with the ORG input/LOW crop diversity system being the most profitable (net returns=$234 ha−1yr−1and net present value=$1953 ha−1). Net returns averaged about 10% less for ORG/DAG compared to the most profitable system, and about 22% less for HIGH/DAG and RED/DAG (the best non-organic systems). The DAP treatments that included forage were not economically competitive with the other treatments, often producing economic losses. The relative profitability of the organic treatments was highly dependent on the existence of organic price premiums. When price premiums for organic crops were reduced to less than 70% of the 2007 levels, the organic treatments were less profitable than the comparable non-organic treatments. The organic treatments also experienced significantly lower (and often negative) net returns compared to the non-organic treatments during completion of the 3-year organic certification period. We estimated that it required 5–7 years after completion of certification for the organic treatments to break even with the comparable non-organic treatments. Thereafter the organic treatments produced consistently higher net earnings. Production costs averaged 16% lower with ORG management compared to the HIGH-input treatments, but we found little difference in total costs between the respective HIGH- and RED-input treatments. The organic treatments also displayed lower income variability than the non-organic treatments, with the ORG/LOW system being preferred by risk-averse producers, who do not subscribe to all-risk crop insurance, and with the ORG/LOW and ORG/DAG systems preferred by low and medium risk-averse producers when having the added financial protection from the Canada/Saskatchewan all-risk crop insurance program.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.478

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.172
Teacher spread0.158 · 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 designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations45
Published2011
Admission routes2
Has abstractyes

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