Long-term effects of a single compaction by heavy field traffic on yield and nitrogen uptake of annual crops

Alakukku, Laura; Elonen, Paavo

doi:10.1016/0167-1987(95)00503-x

Cited by 57 publications

(26 citation statements)

References 19 publications

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“…Disturbance of soil physical properties and/or soil structure are commonly reported effects associated with the use of heavy machinery in agriculture (Voorhees et al, 1986;Alakukku and Elonen, 1995), forestry (Hatchell et al, 1970), and military training (Iverson et al, 1981;Prose, 1985;Braunack, 1986;Thurow et al, 1993;Milchunas et al, 1999). At Fort Benning, Georgia, field training with tracked vehicles has resulted in an overall loss of soil quality at some training sites (Garten et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Modeling Soil Quality Thresholds to Ecosystem Recovery at Fort Benning, Georgia, USA

Garten¹

2004

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Section: Introductionmentioning

confidence: 99%

Modeling Soil Quality Thresholds to Ecosystem Recovery at Fort Benning, Georgia, USA

Garten¹

2004

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“…Such a deficiency would have the plant vulnerable to biotic, biological and nutritional stresses and consequently would lead to reduced productivity (Dourado Neto et al, 2014). As SRP 1 and Des 2 variables are used to assess the state of soil compaction, their effect on soybean yield is the opposite, for plants exhibit alterations in depth, branch and distribution of roots in response to soil compaction (Rosolem et al, 2002), which undermines the efficient use of nutrients and water and limits crop yield (Alakukku & Elomen, 1995).…”

mentioning

confidence: 99%

Soybean yield modeling using bootstrap methods for small samples

Dalposso

Uribe-Opazo

Johann

2016

Span. j. agric. res.

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One of the problems that occur when working with regression models is regarding the sample size; once the statistical methods used in inferential analyzes are asymptotic if the sample is small the analysis may be compromised because the estimates will be biased. An alternative is to use the bootstrap methodology, which in its non-parametric version does not need to guess or know the probability distribution that generated the original sample. In this work we used a set of soybean yield data and physical and chemical soil properties formed with fewer samples to determine a multiple linear regression model. Bootstrap methods were used for variable selection, identification of influential points and for determination of confidence intervals of the model parameters. The results showed that the bootstrap methods enabled us to select the physical and chemical soil properties, which were significant in the construction of the soybean yield regression model, construct the confidence intervals of the parameters and identify the points that had great influence on the estimated parameters.

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“…Soil compaction increases bulk density and soil strength (which restricts the rooting ability of the crop) and decreases porosity and the water infiltration rate. Alakukku and Elonen (1995) studied the long-term effects of compaction on yield and nitrogen uptake in clay and organic soils and reported that the mean result of the first eight years indicated that the compaction of clay soil from four passes reduced the yields by 4% and the nitrogen uptake of the annual crop by 9%. Ngunjiri and Siemens (1995) also reported that maize yield following compaction over the entire plot area averaged 9.8 t ha −1 , which was significantly lower than yields from no compaction, compaction between rows and compaction on the rows, which averaged 12.5, 12.6 and 12.6 t ha −1 , respectively.…”

Section: Introductionmentioning

confidence: 99%

Effects of Machine-Induced Soil Compaction on Growth and Yield of Sugarcane

Usaborisut¹,

Niyamapa²

2010

American Journal of Agricultural and Biological Sciences

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Problem statement: Sugarcane is one of the main economic crops in Thailand. After planting, it can be harvested annually for several successive ratoon crops. Recently, soil compaction due to mechanization has been recognized as a serious problem in sugarcane production. Therefore, this study aimed to clarify the effects of soil compaction on the growth and yield of sugarcane. Approach: The field experiments were conducted in loamy soil using a Randomized Complete Block Design (RCBD) with four treatments of soil compaction prepared by 0, 5, 15 and 20 numbers of wheel passages of a tractor. Results: The results showed that soil compaction had significant effects on both the growth and yield of sugarcane, with the exceptions of tillering and Brix. The greatest reduction in yield compared with the control field was 22.9%, which resulted from compacting with 15 tractor passages. The influence of block or furrow irrigation was indicated by the positive effect that higher watering had on minimizing the impact of soil compaction on the growth of sugarcane. Conclusion: Effects of soil compaction on growth and yield of sugarcane were clarified to some extent.

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Long-term effects of a single compaction by heavy field traffic on yield and nitrogen uptake of annual crops

Cited by 57 publications

References 19 publications

Modeling Soil Quality Thresholds to Ecosystem Recovery at Fort Benning, Georgia, USA

Modeling Soil Quality Thresholds to Ecosystem Recovery at Fort Benning, Georgia, USA

Soybean yield modeling using bootstrap methods for small samples

Effects of Machine-Induced Soil Compaction on Growth and Yield of Sugarcane

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