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Management and Inherent Soil Propert...

Amsili, Joseph Pierre,

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Management and Inherent Soil Properties Shape Soil Health Indicators and Soil Organic Carbon Stocks /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Management and Inherent Soil Properties Shape Soil Health Indicators and Soil Organic Carbon Stocks // Joseph Pierre Amsili.
Author:	Amsili, Joseph Pierre,
Description:	1 electronic resource (300 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
Subject:	Soil sciences. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31483526
ISBN:	9798384050186

Management and Inherent Soil Properties Shape Soil Health Indicators and Soil Organic Carbon Stocks /
Amsili, Joseph Pierre,

Management and Inherent Soil Properties Shape Soil Health Indicators and Soil Organic Carbon Stocks /Joseph Pierre Amsili. - 1 electronic resource (300 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Pedoclimatic context often defines a soil's basic functions, but human management can have superimposing impacts on soil health and carbon (C) stocks. Therefore, it can be challenging to interpret soil health measurements, define benchmarks, and predict the effects of management on soil organic carbon (SOC) stocks in the context of a region's climates, soils, and cropping systems. In chapter 2, I examined the effects of soil texture, a dominant inherent soil property, and cropping system on soil health indicators in New York State (NYS), USA, soils. Available water capacity measured on disturbed samples was mostly affected by texture, while soil respiration, protein, and wet aggregate stability were mostly impacted by cropping system. Pasture and Mixed Vegetable systems tended to have the highest biological and physical soil health and Annual Grain and Processing Vegetable cropping systems had the lowest. In chapter 3, I developed production environment soil health (PESH) benchmarks for eight physical and biological indicators in the context of region, soil texture, and cropping system. Long Island had lower PESH benchmarks for soil organic matter (SOM) than the rest of NYS, implying that regional PESH benchmarks within a state or region are warranted if the pedoclimatic context varies greatly.In chapter 4, I assessed the effects of tillage system on SOC stocks in long-term continuous corn silage and corn grain experiments. No-till did not lead to consistent benefits in SOC stocks relative to plow-till, across the experiments. The differences in SOC stocks between tillage treatments can be explained by pedoclimatic variables and the amount of C inputs.Prediction of soil health indicators that are expensive to measure can improve the cost-effectiveness of comprehensive assessment of soil health. In chapter 5, I developed pedotransfer functions for available water capacity, field capacity (FC), and permanent wilting point (PWP) using random forest (RF) and traditional multiple linear regression modeling. In chapter 6, pedotransfer functions for soil protein were developed using RF and traditional multiple linear regression modeling. Soil protein was sensitive to management at 36 of 57 long-term experiments and the full RF model was able to predict 92% of those significant effects.

English

ISBN: 9798384050186Subjects--Topical Terms:

2122699
Soil sciences.
Subjects--Index Terms:

Soil health

Management and Inherent Soil Properties Shape Soil Health Indicators and Soil Organic Carbon Stocks /
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245 1 0 $a Management and Inherent Soil Properties Shape Soil Health Indicators and Soil Organic Carbon Stocks / $c Joseph Pierre Amsili.
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300 $a 1 electronic resource (300 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
500 $a Advisors: Van Es, Harold Committee members: Ryan, Matthew; Lehmann, Christopher.
502 $b Ph.D. $c Cornell University $d 2024.
520 $a Pedoclimatic context often defines a soil's basic functions, but human management can have superimposing impacts on soil health and carbon (C) stocks. Therefore, it can be challenging to interpret soil health measurements, define benchmarks, and predict the effects of management on soil organic carbon (SOC) stocks in the context of a region's climates, soils, and cropping systems. In chapter 2, I examined the effects of soil texture, a dominant inherent soil property, and cropping system on soil health indicators in New York State (NYS), USA, soils. Available water capacity measured on disturbed samples was mostly affected by texture, while soil respiration, protein, and wet aggregate stability were mostly impacted by cropping system. Pasture and Mixed Vegetable systems tended to have the highest biological and physical soil health and Annual Grain and Processing Vegetable cropping systems had the lowest. In chapter 3, I developed production environment soil health (PESH) benchmarks for eight physical and biological indicators in the context of region, soil texture, and cropping system. Long Island had lower PESH benchmarks for soil organic matter (SOM) than the rest of NYS, implying that regional PESH benchmarks within a state or region are warranted if the pedoclimatic context varies greatly.In chapter 4, I assessed the effects of tillage system on SOC stocks in long-term continuous corn silage and corn grain experiments. No-till did not lead to consistent benefits in SOC stocks relative to plow-till, across the experiments. The differences in SOC stocks between tillage treatments can be explained by pedoclimatic variables and the amount of C inputs.Prediction of soil health indicators that are expensive to measure can improve the cost-effectiveness of comprehensive assessment of soil health. In chapter 5, I developed pedotransfer functions for available water capacity, field capacity (FC), and permanent wilting point (PWP) using random forest (RF) and traditional multiple linear regression modeling. In chapter 6, pedotransfer functions for soil protein were developed using RF and traditional multiple linear regression modeling. Soil protein was sensitive to management at 36 of 57 long-term experiments and the full RF model was able to predict 92% of those significant effects.
546 $a English
590 $a School code: 0058
650 4 $a Soil sciences. $3 2122699
650 4 $a Plant sciences. $3 3173832
653 $a Soil health
653 $a Soil organic carbon
653 $a Soil texture
653 $a Sustainable agriculture
690 $a 0481
690 $a 0479
690 $a 0474
710 2 $a Cornell University. $b Soil and Crop Sciences. $e degree granting institution. $3 3821638
720 1 $a Van Es, Harold $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-03B.
790 $a 0058
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31483526