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Journal ArticleDOI

Effects of No-Till on Yields as Influenced by Crop and Environmental Factors

TLDR
In this paper, the authors evaluated differences in yields and associated downside risk from using no-till and tillage practices across the United States and evaluated with respect to six crops and environmental factors including geographic location, annual precipitation, soil texture, and time since conversion from tillage to notill.
Abstract
Th is research evaluated diff erences in yields and associated downside risk from using no-till and tillage practices. Yields from 442 paired tillage experiments across the United States were evaluated with respect to six crops and environmental factors including geographic location, annual precipitation, soil texture, and time since conversion from tillage to no-till. Results indicated that mean yields for sorghum [Sorghum bicolor (L.) Moench] and wheat (Triticum aestivum L.) with no-till were greater than with tillage. In addition, no-till tended to produce similar or greater mean yields than tillage for crops grown on loamy soils in the Southern Seaboard and Mississippi Portal regions. A warmer and more humid climate and warmer soils in these regions relative to the Heartland, Basin and Range, and Fruitful Rim regions appear to favor no-till on loamy soils. With the exception of corn (Zea mays L.) and cotton (Gossypium hirsutum L.) in the Southern Seaboard region, no-till performed poorly on sandy soils. Crops grown in the Southern Seaboard were less likely to have lower no-till yields than tillage yields on loamy soils and thus had lower downside yield risk than other farm resource regions. Consistent with mean yield results, soybean [Glycine max (L.) Merr.] andmore » wheat grown on sandy soils in the Southern Seaboard region using no-till had larger downside yield risks than when produced with no-till on loamy soils. Th e key fi ndings of this study support the hypothesis that soil and climate factors impact no-till yields relative to tillage yields and may be an important factor infl uencing risk and expected return and the adoption of the practice by farmers.« less

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Citations
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Journal ArticleDOI

When does no-till yield more? A global meta-analysis

TL;DR: The authors conducted a meta-analysis to evaluate the influence of various crop and environmental variables on no-till relative to conventional tillage yields using data obtained from peer-reviewed publications (678 studies with 6005 paired observations, representing 50 crops and 63 countries).
Journal ArticleDOI

Increasing Crop Diversity Mitigates Weather Variations and Improves Yield Stability

TL;DR: Although the magnitude of rotation benefits varied with crops, weather patterns and tillage, yield stability significantly increased when corn and soybean were integrated into more diverse rotations and the benefits of crop diversity under different soil moisture and temperature scenarios.
Journal ArticleDOI

Long-Term No-Till Impacts on Organic Carbon and Properties of Two Contrasting Soils and Corn Yields in Ohio

TL;DR: Kumar et al. as discussed by the authors studied the long-term no-till impacts on organic carbon and properties of two Contrasting Soils and Corn Yields in Ohio Soil & Water Management & Conservation.
Journal ArticleDOI

Thirty-year tillage effects on crop yield and soil fertility indicators

TL;DR: In this article, a long-term study of corn and soybean rotation and 27 years of continuous corn in central Iowa, U.S.A. was conducted to evaluate the effect of no-tillage on productivity and soil fertility.
Journal ArticleDOI

A global meta-analysis of greenhouse gases emission and crop yield under no-tillage as compared to conventional tillage

TL;DR: A global meta-analysis was performed by using fifty peer-reviewed publications to assess the effectiveness of soil physicochemical properties, nitrogen (N) fertilization, type and duration of crop, water management and climatic zones on GHGs emissions and crop yields under NT compared to conventional tillage (CT) practices, and reveals that compared to CT, NT increased CO2, N2O, and CH4 emissions.
References
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Book

Statistical Methods for Meta-Analysis

TL;DR: In this article, the authors present a model for estimating the effect size from a series of experiments using a fixed effect model and a general linear model, and combine these two models to estimate the effect magnitude.
Book

SAS System for Mixed Models

Journal ArticleDOI

Statistical Methods for Meta-Analysis.

TL;DR: In this paper, the authors present a model for estimating the effect size from a series of experiments using a fixed effect model and a general linear model, and combine these two models to estimate the effect magnitude.
Book

The Theory and Practice of Econometrics

TL;DR: The Classical Inference Approach for the General Linear Model, Statistical Decision Theory and Biased Estimation, and the Bayesian Approach to Inference are reviewed.
Book

Regression Analysis by Example

TL;DR: Simple linear regression Multiple linear regression Regression Diagnostics: Detection of Model Violations Qualitative Variables as Predictors Transformation of Variables Weighted Least Squares The Problem of Correlated Errors Analysis of Collinear Data Biased Estimation of Regression Coefficients Variable Selection Procedures Logistic Regression Appendix References as discussed by the authors
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