A Unifying Quantitative Analysis of Soil Texture

Shirazi, Mostafa A.; Boersma, L.

doi:10.2136/sssaj1984.03615995004800010026x

Cited by 414 publications

(177 citation statements)

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“…The categories of MAP used in the meta analysis (Table 1) were based on precipitation and approximated FAO guidelines for agro-climatic zoning (Fischer et al 2001). Soil textures ranged from sandy (2% clay; 98% sand) to clayey (75% clay; 10% sand) and the categorical textural classes are based on the textural triangle (Shirazi and Boersma 1984). Crop yield responses, interactive effects of combined application of ORs and N fertilizers, agronomic N use efficiency and SOC changes were used as the response variables.…”

Section: Database Compilationmentioning

confidence: 99%

Does the combined application of organic and mineral nutrient sources influence maize productivity? A meta-analysis

2010

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The combined application of organic resources (ORs) and mineral fertilizers is increasingly gaining recognition as a viable approach to address soil fertility decline in sub-Saharan Africa (SSA). We conducted a meta-analysis to provide a comprehensive and quantitative synthesis of conditions under which ORs, N fertilizers, and combined ORs with N fertilizers positively or negatively influence Zea mays (maize) yields, agronomic N use efficiency and soil organic C (SOC) in SSA. Four OR quality classes were assessed; classes I (high quality) and II (intermediate quality) had >2.5% N while classes III (intermediate quality) and IV (low quality) had <2.5% N and classes I and III had <4% polyphenol and <15% lignin. On the average, yield responses over the control were 60%, 84% and 114% following the addition of ORs, N fertilizers and ORs + N fertilizers, respectively. There was a general increase in yield responses with increasing OR quality and OR-N quantity, both when ORs were added alone or with N fertilizers. Surprisingly, greater OR residual effects were observed with high quality ORs and declined with decreasing OR quality. The greater yield responses with ORs + N fertilizers than either resource alone were mostly due to extra N added and not improved N utilization efficiency because negative interactive effects were, most often, observed when combining ORs with N fertilizers. Additionally, their agronomic N use efficiency was not different from sole added ORs but lower than N fertilizers added alone. Nevertheless, positive interactive effects were observed in sandy soils with low quality ORs whereas agronomic use efficiency was greater when smaller quantities of N were added in all soils. Compared to sole added ORs, yield responses for the combined treatment increased with decreasing OR quality and greater yield increases were observed in sandy (68%) than clayey soils (25%). While ORs and ORs + N fertilizer additions increased SOC by at least 12% compared to the control, N fertilizer additions were not different from control suggesting that ORs are needed to increase SOC. Thus, the addition of ORs will likely improve nutrient storage while crop yields are increased and more so for high quality ORs. Furthermore, interactive effects are seldom occurring, but agronomic N use efficiency of ORs + N fertilizers were greater with low quantities of N added, offering potential for increasing crop productivity.

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Section: Database Compilationmentioning

confidence: 99%

Does the combined application of organic and mineral nutrient sources influence maize productivity? A meta-analysis

2010

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“…For the other two groups, the Hungarian and US soils, we estimated the distribution at 63 Am. Geometric mean diameter and its standard deviation were calculated according to Shirazi and Boersma (1984). These measures were first calculated using all measured particle-size data available for each soil.…”

Section: Soil Datamentioning

confidence: 99%

Evaluation of different representations of the particle-size distribution to predict soil water retention

Nemes

Rawls

2006

Geoderma

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“…The geometric mean diameter (GMD) was calculated by the following equations as given by Shirazi and Boersma (1984):…”

Section: Methodsmentioning

confidence: 99%

“…Cosby et al (1984) represented the equation for estimating the hydraulic conductivity: Campbell (1985) calculates the parameter b in Eq. 2 by using the geometric mean particle size (Shirazi and Boersma 1984) and the geometric standard deviation of the particle size distribution:…”

Section: Literature Reviewmentioning

confidence: 99%

Pedo-transfer function for saturated hydraulic conductivity of lowland paddy soils

Wayayok¹,

Amin

2009

Paddy Water Environ

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In paddy field, soil saturated hydraulic conductivity (K s ) plays as an important component in the calculation of irrigation requirement of the water balance equation and also for irrigation efficiency. Several laboratory and field methods can be used to determine K s . Laboratory and field determinations are usually time consuming, expensive and labour intensive. Pedo-transfer functions (PTF) serve to translate the basic information found in the soil survey into a form useful for broader applications through empirical regression of functional relationships, such as simulation modelling. Since PTFs have not been applied to paddy soils in the study area, a lot of field measurements will require high labour input to determine K s hence high cost. This study attempts to seek a simplified method for determining K s values based on common existing soil properties through PTF technique. Soil samples (n = 408 samples) were collected randomly depending on the soil series within the 2,300 ha Sawah Sempadan rice cultivation area. Both field work and laboratory work were carried out. The samples were then analysed for the following properties: dry bulk density (D b ), soil particle percentage (Sand-S, Silt-Si and Clay-C), organic matter (OM) and geometric mean diameter (GMD). The measured K s values were obtained by using the falling head method. The parameters were then used as inputs for developing a K s model by regression analysis using Statistical Analysis System (SAS) package. Stepwise regression analysis was applied to determine the best fit model based on R 2 and significant level. The results of the study showed that there is a high spatial variability of the saturated hydraulic conductivity in the paddy area. The best regression model for estimating K s was based on C, D b , OM and GMD with the dependent variable (K s ) in a form of natural logarithm. The model inputs introduced by stepwise regression are commonly available therefore, this model is useful to replace the conventional method.

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A Unifying Quantitative Analysis of Soil Texture

Cited by 414 publications

References 0 publications

Does the combined application of organic and mineral nutrient sources influence maize productivity? A meta-analysis

Does the combined application of organic and mineral nutrient sources influence maize productivity? A meta-analysis

Evaluation of different representations of the particle-size distribution to predict soil water retention

Pedo-transfer function for saturated hydraulic conductivity of lowland paddy soils

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