Técnico, da Universidade de Lisboa. Doctor Fausto Miguel Cereja Seixas Freire, Professor Auxiliar da Faculdade de. Ciências e Tecnologia da Universidade de ...
UNIVERSIDADE DE LISBOA INSTITUTO SUPERIOR TÉCNICO
Soil management: Carbon and Beyond An analysis of selected Portuguese cultivated systems including direct and indirect effects Tatiana Raquel Alves Valada Supervisor: Doctor Tiago Morais Delgado Domingos Thesis approved in public session to obtain the PhD Degree in Environmental Engineering Jury final classification: Pass with merit Jury Chairperson: Chairman of the IST Scientific Board Members of the Committee: Doctor António Jorge Gonçalves de Sousa Doctor Fausto Miguel Cereja Seixas Freire Doctor Gabriel Paulo Alcântara Pita Doctor Tiago Morais Delgado Domingos Doctor Maria da Conceição Pinto Baptista Gonçalves Doctor Maria de Fátima de Sousa Calouro
2014
UNIVERSIDADE DE LISBOA INSTITUTO SUPERIOR TÉCNICO
Soil management: Carbon and Beyond An analysis of selected Portuguese cultivated systems including direct and indirect effects Tatiana Raquel Alves Valada Supervisor: Doctor Tiago Morais Delgado Domingos Thesis approved in public session to obtain the PhD Degree in Environmental Engineering Jury final classification: Pass with merit Jury Chairperson: Chairman of the IST Scientific Board Members of the Committee: Doctor António Jorge Gonçalves de Sousa, Professor Catedrático do Instituto Superior Técnico, da Universidade de Lisboa Doctor Fausto Miguel Cereja Seixas Freire, Professor Auxiliar da Faculdade de Ciências e Tecnologia da Universidade de Coimbra Doctor Gabriel Paulo Alcântara Pita, Professor Auxiliar do Instituto Superior Técnico, da Universidade de Lisboa Doctor Tiago Morais Delgado Domingos, Professor Auxiliar do Instituto Superior Técnico, da Universidade de Lisboa Doctor Maria da Conceição Pinto Baptista Gonçalves, Investigadora Auxiliar do Instituto Nacional de Investigação Agrária e Veterinária, I.P. Doctor Maria de Fátima de Sousa Calouro, Investigadora do Instituto Nacional de Investigação Agrária e Veterinária, I.P.
Instituição Financiadora FCT - Fundação para a Ciência e a Tecnhologia
2014
Para os meus pais
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RESUMO O solo constitui o mais importante reservatório de carbono, tendo a gestão dos sistemas cultivados um papel crucial no balanço do seu impacto para as alterações climáticas. Estes sistemas requerem o uso de factores de produção, como fertilizantes, pesticidas e maquinaria, implicando o uso direto e indireto de recursos e emissão de resíduos para o ecossistema global. Assim, o principal objectivo aqui proposto é a análise do impacto ambiental de sistemas cultivados incluindo efeitos diretos e indiretos. Três questões de investigação são analisadas: (1) Pode o uso de corta-‐mato em pastagens naturais contribuir para o sequestro de carbono no solo? (2) Será a prática de regadio em Portugal uma opção ambientalmente favorável? (3) É o eucalipto um bom substituto para o carvão na geração de energia eléctrica? A abordagem metodológica proposta considera a análise de impactos ambientais diretos e indiretos, uma produção constante de bens alimentares, e o efeito da alteração direta e indireta do uso de solo. Esta mostrou-‐se eficaz, integrada e inovadora, permitindo responder às questões de investigação. No que respeita à gestão de pastagens, para além do sequestro de carbono no solo, foram identificados outros impactos em ciclo de vida, representando um avanço neste tipo de análise. Na avaliação da agricultura de regadio, o pressuposto da produção constante de bens alimentares permitiu um aumento do conhecimento, rejeitando a conclusão difundida do seu balanço negativo de impacto ambiental. Para a análise da substituição de carvão por eucalipto, a inclusão do efeito indireto de alteração de uso do solo permitiu uma avaliação mais conservadora dos impactos ambientais, contrariando a abordagem da sua exclusão, a mais comum na literatura. Palavras-‐chave: sistemas cultivados, sequestro de carbono no solo, impactos ambientais, efeitos indiretos, pastagem, sistema de regadio, produção de eucalipto
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ABSTRACT Soils are the most important reservoirs of carbon, with the management of cultivated systems playing a major role on the climate change impact balance. Moreover, cultivated systems require the use of input factors such as fertilizers, pesticides and machinery, implying a direct and indirect use of resources and emission of residues into the global ecosystem. Here, our main goal is to analyse the environmental impact from agricultural based systems, including direct and indirect effects. Three research questions are considered: (1) Can no-‐tillage contribute to soil sequestration in Portuguese natural grasslands? (2) Is Portuguese irrigated agriculture a good environmental option? and (3) Is eucalyptus a good substitute for coal in electricity generation? The proposed methodological approach considers a set of direct and indirect environmental impacts, constant food production as well as direct and indirect land use change effects. The approach proved to be effective, integrative and innovative, allowing us to answer the research questions. For the grassland analysis, besides the quantified soil carbon sequestration, other direct and indirect impacts were identified, representing an advance in this sort of analysis. For the assessment of irrigated agriculture, the assumption of constant food production allowed a new insight, rejecting the most perceived conclusion of its negative environmental impact balance. For the analysis of eucalyptus as a substitute for coal, the quantification of indirect land use change, allows a more accurate assessment of environmental impacts, lower than the ones obtained excluding it, the most common approach in literature. Keywords: Cultivated systems, soil carbon sequestration, environmental impact, indirect effects, grasslands, irrigated systems, eucalyptus production
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ACKNOWLEDGMENTS I thank my advisor, Tiago Domingos. Without his orientation, bright ideas, motivation, patience and friendship this thesis would not have been possible. I thank Ricardo Teixeira. His support and knowledge, right from the beginning determined my path. He was a mentor, a friend and the companion of many laughs. I thank Helena Martins. She was like a fresh breeze, teaching me how to be a better professional and also a better person. I thank the Terraprima team, Nuno Calado, Nuno Ribeiro, Alfredo Gonçalves Ferreira, Pedro Silveira, António Gonçalves Ferreira, Manuel Ribeiro, Ricardo Vieira, Cristina Marta Pedroso, Carlos Teixeira, Nuno Sarmento, Margarida Gonçalves and Lúcio do Rosário. Their help contributed to a better work in a decisive way. I thank the FCT – Fundação para a Ciência e a Tecnologia for the financial support, through the doctoral scholarship SFRH/BD/40104/2007, funding program PIDDAC. The work was also supported by the projects PETE -‐ Physical Approaches to an Improved Economic Theory of the Environment (FCT, PTDC/AMB/64762/2006); Energy Wars (QREN 7929); and Agridiag -‐ Environmental farm diagnostic tool "Dialecte" in vocational training (LLP-‐LdV-‐ AGRIDIAG TOI-‐2012-‐HU, Agreement number 12/0034-‐L/4850); and by Fenareg, the Portuguese farmers federation for irrigation. Thanks to you all, it was a great journey.
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FULL TABLE OF CONTENTS RESUMO ..................................................................................................................................................... III ABSTRACT .................................................................................................................................................. V ACKNOWLEDGMENTS .......................................................................................................................... VII FULL TABLE OF CONTENTS .................................................................................................................. IX LIST OF FIGURES .................................................................................................................................. XIII LIST OF TABLES ..................................................................................................................................... XV LIST OF NOTES .................................................................................................................................... XVII ACRONYMS AND ABBREVIATIONS ................................................................................................. XIX CHAPTER 1. INTRODUCTION .......................................................................................................... 21 1.1. THE WORLD AS IT IS ..................................................................................................................................... 21 1.2. STATE OF THE ART & CONTRIBUTION ....................................................................................................... 26 CHAPTER 2. METHODOLOGICAL OVERVIEW ............................................................................ 29 2.1. OVERALL APPROACH .................................................................................................................................... 29 2.2. SOIL & CARBON ............................................................................................................................................. 35 2.3. CARBON & BEYOND ...................................................................................................................................... 37 CHAPTER 3. CAN NO-‐TILLAGE CONTRIBUTE TO SOIL CARBON SEQUESTRATION IN PORTUGUESE NATURAL GRASSLANDS? ............................................................................................... 39 3.1. INTRODUCTION ............................................................................................................................................. 40 3.2. METHODS ....................................................................................................................................................... 45 3.2.1 Sampling design ..................................................................................................................................... 45 3.2.2 Statistical analysis of SOM differences ......................................................................................... 50 3.2.2.1 TWO SAMPLE MEAN HYPOTHESIS APPROACH ....................................................................................................................... 50 3.2.2.2 MULTIPLE LINEAR REGRESSION APPROACH ......................................................................................................................... 52
3.2.3 Carbon sequestration factor ............................................................................................................. 53 3.3. RESULTS & DISCUSSION ............................................................................................................................... 54 3.3.1 Statistical analysis of SOM differences ......................................................................................... 54 3.3.1.1 3.3.1.2
TWO SAMPLE MEAN HYPOTHESIS APPROACH ....................................................................................................................... 54 MULTIPLE LINEAR REGRESSION APPROACH .......................................................................................................................... 60
3.3.2 Carbon sequestration factor ............................................................................................................. 62 3.4. CONCLUSIONS ................................................................................................................................................ 67 CHAPTER 4. IS PORTUGUESE IRRIGATED AGRICULTURE A GOOD ENVIRONMENTAL OPTION?................. ......................................................................................................................................... 69 4.1. INTRODUCTION ............................................................................................................................................. 69 4.2. METHODOLOGY ............................................................................................................................................. 73 4.2.1 Overview .................................................................................................................................................... 73 4.2.2 The counterfactual scenario ............................................................................................................. 73 4.3. CHARACTERIZATION OF THE AGRICULTURAL SYSTEM ........................................................................... 76 4.3.1 Input resources ....................................................................................................................................... 76 4.3.2 Output flows and emissions ............................................................................................................... 79 IX
4.4. RESULTS AND DISCUSSION .......................................................................................................................... 81 4.4.1 Water .......................................................................................................................................................... 81 4.4.1.1 4.4.1.2 4.4.1.3
USE .......................................................................................................................................................................................... 81 EUTROPHICATION ................................................................................................................................................................... 85 QUALITY .................................................................................................................................................................................. 89
4.4.2 Land ............................................................................................................................................................. 94 4.4.2.1 4.4.2.2 4.4.2.3
SALINIZATION ......................................................................................................................................................................... 94 ACIDIFICATION ........................................................................................................................................................................ 95 PRODUCTIVE LAND USE .......................................................................................................................................................... 98
4.4.3 Atmosphere ............................................................................................................................................... 99 4.4.3.1 4.4.3.2 4.4.3.3
MICROCLIMATE CHANGE ........................................................................................................................................................ 99 CLIMATE CHANGE ................................................................................................................................................................... 99 OZONE DEPLETION .............................................................................................................................................................. 103
4.4.4 Energy ...................................................................................................................................................... 106 4.4.4.1
FOSSIL FUEL DEPLETION ..................................................................................................................................................... 106
4.5. CONCLUSIONS ............................................................................................................................................. 109 CHAPTER 5. IS EUCALYPTUS A GOOD SUBSTITUTE FOR COAL IN ELECTRICITY GENERATION?...... ...................................................................................................................................... 113 5.1. INTRODUCTION .......................................................................................................................................... 113 5.2. METHODOLOGY .......................................................................................................................................... 115 5.2.1 General approach ............................................................................................................................... 115 5.2.2 Modelling tools ..................................................................................................................................... 115 5.3. CHARACTERIZATION OF THE ANALYSED SYSTEM ................................................................................. 116 5.3.1 Goal and scope ..................................................................................................................................... 116 5.3.2 Inventory ................................................................................................................................................ 119 5.4. RESULTS AND DISCUSSION ....................................................................................................................... 121 5.4.1 Partial analysis .................................................................................................................................... 121 5.4.2 Integrated analysis ............................................................................................................................ 122 5.4.3 Further analysis of the ILUC effect .............................................................................................. 126 5.5. CONCLUSION ............................................................................................................................................... 130 CHAPTER 6. CONCLUSIONS AND FUTURE WORK ................................................................. 131 BIBLIOGRAPHY .................................................................................................................................... 135 ANNEX I. CHAPTER 3: FIELD FORM ................................................................................................. I ANNEX II. CHAPTER 3: AUXILIARY FORM .................................................................................. III ANNEX III. CHAPTER 3: DETAILED DATA ..................................................................................... V ANNEX IV. CHAPTER 3: CAOF DATA ........................................................................................ XXXI ANNEX V. CHAPTER 4: DETAILED RESULTS ...................................................................... XXXIII WATER USE ........................................................................................................................................................ XXXIII EUTROPHICATION .............................................................................................................................................. XXXIV ACIDIFICATION ..................................................................................................................................................... XXXV PRODUCTIVE LAND USE ..................................................................................................................................... XXXVI CLIMATE CHANGE ............................................................................................................................................. XXXVII OZONE DEPLETION .......................................................................................................................................... XXXVIII FOSSIL FUEL DEPLETION ................................................................................................................................... XXXIX X
COMPARISON WITH SIMAPRO DEFAULT AGRICULTURAL PRODUCTS ........................................................ XXXIX ANNEX VI. CHAPTER 5: DETAILED RESULTS ........................................................................ XLIII MAIZE ..................................................................................................................................................................... XLIII TOMATO ................................................................................................................................................................. XLIII BROCCOLI ................................................................................................................................................................ XLIV ANNEX VII. CHAPTER 6: PROCESS BASED MODELS .............................................................. XLV
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LIST OF FIGURES FIGURE 1 – EUROPEAN TOPSOIL ORGANIC CARBON (EEA AND JRC, 2010) .................................................................................. 21 FIGURE 2 – EUROPEAN NITROGEN SURPLUS (EEA AND JRC, 2010) ............................................................................................... 24 FIGURE 3 – SOC STOCK CHANGE DUE TO THE SHIFT IN CULTIVATED SYSTEM ................................................................................. 29 FIGURE 4 – CULTIVATED SYSTEMS AS A SOURCE OF EMISSIONS INTO THE GLOBAL ECOSYSTEM ................................................... 30 FIGURE 5 -‐ OVERALL METHODOLOGICAL APPROACH ........................................................................................................................... 31 FIGURE 6 – CASE STUDY #1: NATURAL GRASSLANDS .......................................................................................................................... 31 FIGURE 7 – CASE STUDY #2: IRRIGATED SYTEM (1/2) ..................................................................................................................... 32 FIGURE 8 – CASE STUDY #2: IRRIGATED SYTEM (2/2) ...................................................................................................................... 32 FIGURE 9 – CASE STUDY #3: EUCALYPTUS AS A RAW MATERIAL TO ELECTRICITY GENERATION ................................................. 33 FIGURE 10 – PHASES OF A LCA AS PRESENTED BY REBITZER ET AL. (2004) ................................................................................ 37 FIGURE 11 – ELEMENTS OF LCIA AS PRESENTED BY PENNINGTON ET AL. (2004) ...................................................................... 38 FIGURE 12 – MAP OF PORTUGAL, WITH THE INDICATION OF THE SAMPLING SITES ....................................................................... 47 FIGURE 13 – FIELD WORK ....................................................................................................................................................................... 48 FIGURE 14 – FIELD FORM ........................................................................................................................................................................ 48 FIGURE 15 – HISTOGRAM OF THE YEARS PASSED SINCE LAST MOBILIZATION FOR THE NO-‐TILLED PLOTS ................................ 51 FIGURE 16 – SOM LEVELS VERSUS YSLM FOR THE ALL DATA .......................................................................................................... 53 FIGURE 17 – SCHEMATIC REPRESENTATION OF SOM EVOLUTION ACCORDING TO THE MANAGEMENT SYSTEM ...................... 53 FIGURE 18 – BOX PLOT FOR SOM DATA BY SYSTEM AND SAMPLING YEAR ..................................................................................... 54 FIGURE 19 – BOX PLOT FOR SOM DATA BY SYSTEM AND SAMPLING YEAR AND ORIGINAL MATERIAL ........................................ 55 FIGURE 20 – DISTRIBUTIONAL DOTPLOT FOR 2011 (LEFT) AND 2012 (RIGHT) SOM DATA .................................................... 55 FIGURE 21 – COMPARISON WITH NORMAL QUARTILE FOR NO-‐TILLAGE (LEFT) AND TILLAGE (RIGHT) SOM DATA, 2011 ... 56 FIGURE 22 – COMPARISON WITH NORMAL QUARTILE FOR NO-‐TILLAGE (LEFT) AND TILLAGE (RIGHT) SOM DATA, 2012 ... 56 FIGURE 23 – DISTRIBUTIONAL DOTPLOT FOR 2011 (LEFT) AND 2012 (RIGHT) LN(SOM) DATA ............................................ 56 FIGURE 24 – COMPARISON WITH NORMAL QUARTILE FOR NO-‐TILLAGE (LEFT) AND TILLAGE (RIGHT) LN(SOM), 2011 ..... 57 FIGURE 25 – COMPARISON WITH NORMAL QUARTILE FOR NO-‐TILLAGE (LEFT) AND TILLAGE (RIGHT) LN(SOM), 2012 ..... 57 FIGURE 26 – SCHEMATIC REPRESENTATION OF T-‐TEST RESULT FOR LN(SOM) DATA (95% CI), 2011 ................................. 58 FIGURE 27 – SCHEMATIC REPRESENTATION OF T-‐TEST RESULT FOR LN(SOM) DATA (95% CI), 2012 ................................. 58 FIGURE 28 – SCHEMATIC REPRESENTATION OF T-‐TEST RESULT FOR SOM DATA (95% CI), 2011 .......................................... 59 FIGURE 29 – SCHEMATIC REPRESENTATION OF T-‐TEST RESULT FOR SOM DATA (95% CI), 2012 .......................................... 59 FIGURE 30 – PREDICTED VERSUS OBSERVED DATA, 2011 (LEFT) AND 2012 (RIGHT) RESULTS ............................................... 61 FIGURE 31 – RESIDUALS VERSUS FITTED VALUES, 2011 (LEFT) AND 2012 (RIGHT) RESULTS .................................................. 61 FIGURE 32 – COMPARISON WITH NORMA L QUANTILE FOR RESIDUALS, 2011 (LEFT) AND 2012 (RIGHT) RESULTS ............. 62 FIGURE 33 – 10-‐YEAR MODEL FOR THE EVOLUTION OF SOM IN SOWN BIODIVERSE GRASSLANDS ............................................. 64 FIGURE 34 – BULK DENSITY DATA DISTRIBUTION ............................................................................................................................... 65 FIGURE 35 – BOX PLOT DISTRIBUTION OF THE BULK DENSITY DATA ............................................................................................... 65 FIGURE 36 – PRINCIPAL AREAS OF IRRIGATION (JONES ET AL., 2012) ........................................................................................... 70 FIGURE 37 – SCHEMATIC REPRESENTATION OF THE ANALYSED SYSTEM ......................................................................................... 74 FIGURE 38 – WATER FOOTPRINT FOR RICE, MAIZE, TOMATO, BROCCOLI AND ORANGE ................................................................ 82 FIGURE 39 – WATER FOOTPRINT CONSIDERING NATIONAL INFORMATION FOR BLUE AND GREEN WATER ESTIMATE ............ 82 FIGURE 40 – NATIONAL WATER SCARCITY ASSESSMENT (INAG, 2002) ........................................................................................ 84 FIGURE 41 – POTENTIAL EUTROPHICATION BY CROP AND SCENARIO .............................................................................................. 86 FIGURE 42 – IMPACT OF INTERNATIONAL TRANSPORT ON POTENTIAL EUTROPHICATION (SEE NOTE 6) ................................. 86 FIGURE 43 – EFFECT OF INPUT DATA ON THE EUTROPHICATION AI ................................................................................................ 87 FIGURE 44 – OVERALL STATTUS OF SURFACE WATER FOR 2011, 2003 AND 1995 .................................................................... 90 FIGURE 45 – SCHEMATIC REPRESENTATION OF THE METHOD USED FOR SURFACE WATER QUALITY ASSESSMENT .................. 92 FIGURE 46 – OVERALL PROCEDURE OF CLASSIFICATION TESTS FOR GROUNDWATER STATUS (COMMISSION, 2009) ............. 94
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FIGURE 47 – POTENTIAL ACIDIFICATION BY CROP AND SCENARIO ................................................................................................... 96 FIGURE 48 – IMPACT OF INTERNATIONAL TRANSPORT ON POTENTIAL ACIDIFICATION RESULTS (SEE NOTE 6) ...................... 96 FIGURE 49 – EFFECT OF INPUT DATA ON THE POTENTIAL ACIDIFICATION AI ................................................................................ 97 FIGURE 50 – LAND OCCUPATION FOR RICE, MAIZE, TOMATO, BROCCOLI, ORANGE AND OLIVE ..................................................... 99 FIGURE 51 – GHG EMISSION BY CROP AND SCENARIO ...................................................................................................................... 100 FIGURE 52 – IMPACT OF INTERNATIONAL TRANSPORT ON GHG RESULTS (SEE NOTE 6) .......................................................... 101 FIGURE 53 – EFFECT OF INPUT DATA ON THE GHG AI ..................................................................................................................... 102 FIGURE 54 – OZONE DEPLETION POTENTIAL BY CROP AND SCENARIO ........................................................................................... 103 FIGURE 55 – IMPACT OF INTERNATIONAL TRANSPORT ON OZONE DEPLETION RESULTS (SEE NOTE 6) .................................. 104 FIGURE 56 – EFFECT OF INPUT DATA ON THE OZONE DEPLETION AI ............................................................................................. 105 FIGURE 57 – FOSSIL FUEL DEPLETION POTENTIAL BY CROP AND SCENARIO ................................................................................. 106 FIGURE 58 – IMPACT OF INTERNATIONAL TRANSPORT ON FOSSIL FUEL DEPLETION RESULTS (SEE NOTE 6) ......................... 107 FIGURE 59 – EFFECT OF INPUT DATA ON THE FOSSIL FUEL DEPLETION AI ................................................................................... 108 FIGURE 60 – GEOGRAPHICAL LOCATION OF THE PRODUCTIVE REGION AND POWER PLANT ....................................................... 117 FIGURE 61 – SYSTEM DEFINITION ........................................................................................................................................................ 118 FIGURE 62 – AI EXCLUDING AND INCLUDING ILUC .......................................................................................................................... 125 FIGURE 63 – AI CONSIDERING TOMATO PRODUCTION IN THE ILUCF ............................................................................................ 127 FIGURE 64 – AI CONSIDERING BROCCOLI PRODUCTION IN THE ILUCF ......................................................................................... 129 FIGURE 65 – TREE COVER AUXILIARY FORM (FERREIRA ET AL., 2009) ........................................................................................... III FIGURE 66 – COMPARISON OF THE SYSTEM ANALYSED WITH DEFAULT SIMAPRO DATA FOR RICE ............................................. XL FIGURE 67 – COMPARISON OF THE SYSTEM ANALYSED WITH DEFAULT SIMAPRO DATA FOR MAIZE ......................................... XLI
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LIST OF TABLES TABLE 1 – CARBON SEQUESTRATION POTENTIAL (SMITH, 2004A) ................................................................................................ 26 TABLE 2 – SCOPE OF THE ANALYSED CASE STUDIES ............................................................................................................................ 34 TABLE 3 – MEASURED GRASSLAND CARBON SEQUESTRATION FACTORS .......................................................................................... 42 TABLE 4 – SAMPLING EFFORT BY MANAGEMENT TECHNIQUE AND ROCK MATERIAL ..................................................................... 46 TABLE 5 – DISTRIBUTION OF SAMPLED PLOTS BY FARM AND MUNICIPALITY .................................................................................. 46 TABLE 6 – DESCRIPTION OF “LABORATORY VARIABLES” .................................................................................................................... 49 TABLE 7 – DESCRIPTION OF “FIELD VARIABLES” ................................................................................................................................. 49 TABLE 8 – DESCRIPTION OF “OFFICE VARIABLES” ............................................................................................................................... 50 TABLE 9 – MEAN AND STANDARD DEVIATION STATISTICS OF YSLM VARIABLES, PER YEAR ....................................................... 51 TABLE 10 – SOM AVERAGE AND STANDARD DEVIATION ................................................................................................................... 54 TABLE 11 – TWO SAMPLE T-‐TEST RESULTS FOR LN(SOM) DATA (95% CI) ................................................................................. 57 TABLE 12 – HIGHER AND LOWER ΔSOM FROM THE T-‐TEST RESULTS (95% CI) ......................................................................... 60 TABLE 13 – VARIABLES NOT HOMOGENEOUS AMONG MANAGEMENT TECHNIQUES (95% CI) ................................................... 60 TABLE 14 – MULTIPLE LINEAR REGRESSION ANALYSIS RESULTS (95% CI) .................................................................................. 61 TABLE 15 – HIGHER AND LOWER ΔSOM OBTAINED FROM THE MULTIPLE LINEAR REGRESSION APPROACH (95% CI) ........ 62 TABLE 16 – CARBON SEQUESTRATION FACTORS ................................................................................................................................. 63 TABLE 17 – CHARACTERIZATION OF BULK DENSITY DATA ................................................................................................................. 65 TABLE 18 – IMPACT DUE TO THE SHIFT FROM TILLAGE TO NO-‐TILLAGE ......................................................................................... 67 TABLE 19 – ANALYSED TOPICS ............................................................................................................................................................... 71 TABLE 20 – GENERAL APPROACH .......................................................................................................................................................... 73 TABLE 21 – COUNTERFACTUAL SCENARIO BY CROP ............................................................................................................................ 73 TABLE 22 – MAIN COUNTRIES OF IMPORTS FOR RICE, MAIZE, TOMATO, BROCCOLI AND ORANGE ............................................... 75 TABLE 23 – COMBINATION OF DATA ASSUMED IN THE ANALYSIS ..................................................................................................... 75 TABLE 24 – PRODUCTIVITY AND FERTILIZER INPUT ........................................................................................................................... 77 TABLE 25 – PESTICIDE INPUT ................................................................................................................................................................. 78 TABLE 26 – MECHANICAL OPERATIONS ................................................................................................................................................ 78 TABLE 27 – DISTANCE OF TRANSPORT FOR IMPORTED CROPS .......................................................................................................... 79 TABLE 28 – EMISSIONS FROM FERTILIZER APPLICATION AND FLOODED RICE ................................................................................ 80 TABLE 29 – NATIONAL WATER BALANCE ............................................................................................................................................. 84 TABLE 30 – ASSESSMENT OF IRRIGATION ON THE POTENTIAL EUTROPHICATION IMPACT ........................................................... 88 TABLE 31 – TEMPORAL EVOLUTION OF SURFACE WATER QUALITY .................................................................................................. 89 TABLE 32 – EVALUATION OF THE NATIONAL SURFACE WATER QUALITY ........................................................................................ 91 TABLE 33 – GROUNDWATER QUALITY ASSESSMENT (1/2) .............................................................................................................. 92 TABLE 34 – CLASSIFICATION SCALE FOR THE GROUNDWATER QUALITY ......................................................................................... 93 TABLE 35 – GROUNDWATER QUALITY ASSESSMENT (2/2) .............................................................................................................. 93 TABLE 36 – ASSESSMENT OF IRRIGATION ON THE POTENTIAL ACIDIFICATION IMPACT ................................................................ 98 TABLE 37 – ASSESSMENT OF IRRIGATION ON THE POTENTIAL GHG IMPACT ............................................................................... 102 TABLE 38 – ASSESSMENT OF IRRIGATION ON THE POTENTIAL OZONE DEPLETION IMPACT ....................................................... 105 TABLE 39 – ASSESSMENT OF IRRIGATION ON THE POTENTIAL FOSSIL FUEL DEPLETION IMPACT .............................................. 108 TABLE 40 – PRODUCTION VERSUS CONSUMPTION OF ENERGY ........................................................................................................ 109 TABLE 41 – IRRIGATED VS COUNTERFACTUAL SCENARIO: SUMMARY OF RESULTS ...................................................................... 111 TABLE 42 – IMPACT OF IRRIGATION ON RESULTS .............................................................................................................................. 111 TABLE 43 – DESCRIPTION OF FIGURE 61 ........................................................................................................................................... 118 TABLE 44 – FUELS INFORMATION AT COAL-‐FIRED POWER PLANT PEGO (FUNCTIONAL UNIT: 1 KWHE) ................................ 120 TABLE 45 – LCA IMPACTS AND AI FOR THE COMPARISON BETWEEN COMBUSTION AND CO-‐FIRING ........................................ 121 TABLE 46 – LCA TOTAL IMPACTS OF EUCALYPTUS AND MAIZE ....................................................................................................... 121
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TABLE 47 – COMPARISON OF THE OBTAINED AI WITH LITERATURE ............................................................................................. 122 TABLE 48 – IMPACT PER KWHE ........................................................................................................................................................... 123 TABLE 49 – SUMMARY OF THE AI RESULTS ........................................................................................................................................ 130 TABLE 50 – SUMMARY OF THE CASE STUDIES .................................................................................................................................... 133 TABLE 51 – SUMMARY OF RESULTS ..................................................................................................................................................... 134 TABLE 52 – MAIN DATA USED IN THE NATURAL GRASSLND ANALYSIS ................................................................................................ V TABLE 53 – DATA FOR THE “LABORATORY VARIABLES” ..................................................................................................................... IX TABLE 54 – DATA FOR THE “FIELD VARIABLES” (1/2) .................................................................................................................... XIII TABLE 55 – DATA FOR THE “FIELD VARIABLES” (2/2) ................................................................................................................. XVIII TABLE 56 – DATA FOR THE “OFFICE VARIABLES” ............................................................................................................................. XXII TABLE 57 – SOM DATA FOR “QUINTA DA FRANÇA” ...................................................................................................................... XXVII TABLE 58 – SOM DATA FOR “COMPANHIA DAS LEZÍRIAS” ........................................................................................................... XXVII TABLE 59 – SOM DATA FOR “OTHERS” .......................................................................................................................................... XXVIII TABLE 60 –DATA FOR BULK DENSITY ANALYSIS .............................................................................................................................. XXIX TABLE 61 – WATER FOOTPRINT BY CROP AND SCENARIO ........................................................................................................... XXXIII TABLE 62 – WATER FOOTPRINT BY COUNTRY OF THE COUTERFACTUAL SCENARIO ............................................................... XXXIII TABLE 63 – WATER FOOTPRINT INCLUDING DETAILED INFORMATION FROM THE NI ........................................................... XXXIV TABLE 64 – WATER FOOTPRINT INCLUDING INFORMATION FROM THE NATIONAL GDARD ................................................ XXXIV TABLE 65 – DETAILED RESULTS FOR THE EUTROPHICATION TOPIC .......................................................................................... XXXIV TABLE 66 – RESULTS FOR THE EUTROPHICATION TOPIC CONSIDERING DIFFERENT SETS OF DATA ...................................... XXXV TABLE 67 – DETAILED RESULTS FOR THE ACIDIFICATION TOPIC ................................................................................................ XXXV TABLE 68 – RESULTS FOR THE ACIDIFICATION TOPIC CONSIDERING DIFFERENT SETS OF DATA .......................................... XXXVI TABLE 69 – DETAILED RESULTS FOR THE PRODUCTIVE LAND USE TOPIC ................................................................................. XXXVI TABLE 70 – DETAILED RESULTS FOR THE CLIMATE CHANGE TOPIC ........................................................................................ XXXVII TABLE 71 – RESULTS FOR THE CLIMATE CHANGE TOPIC CONSIDERING DIFFERENT SETS OF DATA .................................... XXXVII TABLE 72 – DETAILED RESULTS FOR THE OZONE DEPLETION TOPIC ..................................................................................... XXXVIII TABLE 73 – RESULTS FOR OZONE DEPLETION TOPIC CONSIDERING DIFFERENT SETS OF DATA ......................................... XXXVIII TABLE 74 – DETAILED RESULTS FOR THE FOSSIL FUEL DEPLETION TOPIC ............................................................................... XXXIX TABLE 75 – RESULTS FOR THE FOSSIL FUEL DEPLETION TOPIC CONSIDERING DIFFERENT SETS OF DATA .......................... XXXIX TABLE 76 – DETAILED RESULTS FOR THE NATIONAL PRODUCTION OF MAIZE ............................................................................ XLIII TABLE 77 – DETAILED RESULTS FOR THE PRODUCTION OF MAIZE IN FOREIGN COUNTRIES ..................................................... XLIII TABLE 78 – DETAILED RESULTS FOR THE NATIONAL PRODUCTION OF TOMATO ........................................................................ XLIII TABLE 79 – DETAILED RESULTS FOR THE PRODUCTION OF TOMATO IN FOREIGN COUNTRIES ................................................. XLIV TABLE 80 – DETAILED RESULTS FOR THE NATIONAL PRODUCTION OF BROCCOLI ..................................................................... XLIV TABLE 81 – DETAILED RESULTS FOR THE PRODUCTION OF BROCCOLI IN FOREIGN COUNTRIES .............................................. XLIV TABLE 82 – OVERVIEW OF SOM MODELS (FALLOON AND SMITH, 2009) .................................................................................. XLV TABLE 83 – ROTHC VERSUS CENTURY: A SYSTEMATIC COMPARISON ..................................................................................... XLVII
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LIST OF NOTES NOTE 1 – SOM DEFINITION ..................................................................................................................................................................... 22 NOTE 2 – ENVIRONMENTAL IMPACT DEFINITION ................................................................................................................................ 22 NOTE 3 – DIRECT AND INDIRECT ENVIRONMENTAL IMPACT DEFINITION ........................................................................................ 23 NOTE 4 – DLUC AND ILUC DEFINITION ............................................................................................................................................... 25 NOTE 5 – WATER FOOTPRINT DEFINITION ........................................................................................................................................... 83 NOTE 6 – HOW TO READ FIGURE 42 ...................................................................................................................................................... 88 NOTE 7 – AI DEFINITION ......................................................................................................................................................................... 88 NOTE 8 – IRRIGATION IMPACT ................................................................................................................................................................ 89 NOTE 9 – AGRICULTURAL SIMULATION DATA .................................................................................................................................... 123
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ACRONYMS AND ABBREVIATIONS AI CI
Avoided impact Confidence interval
DLUC d.m.
Direct land use change
EEA
European Environment Agency
EU
European Union
FAO
Food and Agriculture Organization of the United Nations
FAOSTAT
Statistics division of the FAO
FCs
Foreign countries
GDARD
General Directorate for Agriculture and Rural Development
GHG
Greenhouse gas
HWSDB
Harmonized World Soil Data Base
IFA
International Fertilizer Industry Association
ILUC
Indirect land use change
ILUCF
Indirect land use change factor
IPCC
Intergovernmental Panel on Climate Change
ISO
International Organization for Standardization
JRC
Joint Research Centre
LCA
Life cycle assessment
LCI
Life cycle inventory
LCIA
Life cycle impact assessment
LQARS
Laboratório Químico Agrícola Rebelo da Silva
LULUCF
Land Use, Land Use Change and Forestry
NFI
National Forest Inventory
NI
National inventory
NILUC
Neglecting indirect land use change
NR
Nutrient removal
o.d.t.
Oven dry tonnes
OECD
Organisation for Economic Cooperation and Development
PCF
Portuguese Carbon Fund
KP
Kyoto Protocol
RO
Ribatejo e Oeste
SOM
Soil organic matter
SOC
Soil organic carbon
SRC
Short rotation coppice
UNFCCC
United Nations Framework Convention on Climate Change
Dry matter
XIX
Chapter 1. I NTRODUCTION 1.1.
T H E W O R L D A S IT IS
World’s soils contain about 1500 Pg of organic carbon (Batjes, 1996), three times the amount of carbon in vegetation and twice the amount in atmosphere (Smith et al., 2009). According to an estimate from 2004, since industrial revolution depletion of soil organic carbon (SOC) pool have globally contributed with 78 ± 12 PgC to the atmosphere, comparing with 270 ± 30 PgC from fossil fuel combustion (Lal, 2004). Some cultivated soils have lost one half to two thirds of the original SOC pool with a cumulative loss of 20 -‐ 40 MgC.ha-‐1 (Lal, 2004). Since depletion of soil C is exacerbated by land misuse and soil mismanagement, the adoption of restorative land use and recommended management practices may contribute to SOC restoration (Lal, 2004; Wollenberg et al., 2012). Global potential of SOC sequestration through the adoption of recommended management practices is 0.9 ± 0.3 PgC.yr-‐1, which may offset one-‐fourth to one-‐third of the annual increase in atmospheric CO2, estimated at 3.3 PgC.yr-‐1 (Lal, 2004). In generic terms, the most direct way to achieve soil carbon sequestration is by increasing soil organic matter (SOM) inputs (see Note 1) or by slowing SOM decomposition (Smith et al., 2009). As for management techniques, literature options runs from zero/reduced tillage and conversion to permanent crops, to improved management to reduce wind and water erosion (Smith, 2004b). In order to maximize sink potential, the best options are to increase carbon stocks in soils that have been depleted in carbon (Smith et al., 2009). According to Figure 1 Portugal presents a low topsoil organic carbon, with a high potential of improvement.
F IGURE 1 – E UROPEAN TOPSOIL ORGANIC CARBON (EEA AND JRC, 2010)
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These facts put soil management at the center of the climate change policy discussion, as a way to reduce environmental impact (see Note 2). This was already recognized by the United Nations Framework Convention on Climate Change (UNFCCC) (Schlamadinger et al., 2007; UNFCCC, 1992). In the Kyoto Protocol (KP), although there are strict stipulations as to how a country’s emissions inventory is made, there are some items related to the agro-‐forestry sector that remain optional for each signatory country. These options are the so-‐called Land Use, Land Use Change and Forestry (LULUCF) activities, under the framework of Article 3.4 of the KP. Portugal plays a leading role in its KP accounts, since it decided to elect, in the framework of these voluntary activities, the ones regarding “Grassland management”, “Cropland Management” and “Forest Management”. Although soil carbon sequestration can be achieved through the increase of SOM concentration, there is much more about SOM than only carbon sequestration. SOM concentration affects the chemical and physical properties of soil and its overall health. Its composition and breakdown rate affects soil structure and porosity, water infiltration rate and moisture holding capacity of soils, diversity and biological activity of soil organisms and plant nutrient availability (Bot and Benites, 2005). Restoring soil carbon is essential to enhance soil quality, sustain and improve food production and maintain clean water. Given the above, the increase in SOM contributes to decrease the risk of desertification, soil erosion, floods and soil biodiversity, answering to some of the main threats to soil identified by Communities (2006). N OTE 1 – SOM DEFINITION
SOM can be defined as “the plant and animal remains at different stages of decomposition and the substances derived from the biological activity of the soil-‐living population” (Soil Science Society of America, 1984 in Rodeghiero et al., 2009). SOC is the carbon content of the SOM. SOC content of SOM is mostly around 50 to 60% (Rodeghiero et al., 2009), with 58% as the most common value in literature (Costa, 2011; Guo and Gifford, 2002; Post et al., 2001). Commonly, soils are characterized by a SOM rich topsoil with decreasing soil organic content down the soil profile, its thickness determined by soil mixing (Rodeghiero et al., 2009). N OTE 2 – E NVIRONMENTAL IMPACT DEFINITION
According to the International Organization of Standardization (ISO) 14001 Environmental Systems Handbook, and quoting the original ISO manuscript, an environmental impact correspond to (Whitelaw, 2004): “Any change to the environment, whether adverse or beneficial, wholly or partially resulting from an organization’s activities, products or services” When regarding cultivated systems, soil carbon sequestration is not the only contributor to climate change. Direct impacts from greenhouse gas (GHG) emissions occur along all the cultivation process (for example through the use of machinery), and not only in the form of CO2 (see Note 3). While N2O emissions occur from addition of N to soil (for example in the form of
22
synthetic fertilizer or residues), CH4 emissions take place associated with flooded rice, cattle enteric fermentation and animal residues. As an indirect effect, the production and transport of input factors to the cultivation site, as fertilizers, pesticides, seeds and machinery also implies the emission of GHG. N OTE 3 – D IRECT AND INDIRECT ENVIRONMENTAL IMPACT DEFINITION
According to Whitelaw (2004), environmental impacts can be categorized into two groups, namely, direct and indirect impacts. Quoting the author: “A direct impact is a change arising as a direct result of an activity under the control of the organization. An indirect impact is a change that arises as a result of someone else’s activities; these activities are connected to the organization in some way but are less easily controlled as they can only be influenced indirectly”. According to Bellarby et al. (2008), for the year of 2005, agriculture contributed with direct emissions of 5.1 -‐ 6.1 PgCO2eq (10 -‐ 12%) to global GHG emissions. These emissions were mainly in the form of methane (3.3 PgCO2eq.yr-‐1) and nitrous oxide (2.8 PgCO2eq.yr-‐1) whereas the net flux of carbon dioxide was very small (0.04 PgCO2eq.yr-‐1). Considering the average global emissions, nitrous oxide emissions from soils and methane from enteric fermentation of cattle constituted the largest sources, 38% and 32% of total non-‐CO2 emissions from agriculture, respectively. Biomass burning (12%), rice production (11%), manure management (7%), accounted for the rest (Bellarby et al., 2008). From these accounts were excluded farm machinery, fertilizer and pesticide production, which were responsible for the emission of 0.2 PgCO2eq.yr-‐1, 0.4 PgCO2eq.yr-‐1 and 0.07 PgCO2eq.yr-‐1, respectively (Bellarby et al., 2008). The magnitude and relative importance of the different sources and emissions vary widely among regions. Globally, agricultural methane and nitrous oxide emissions have increased by 17% from 1990 to 2005, and are projected to increase by another 35 -‐ 60% by 2030, driven by growing nitrogen use and increased livestock production (Bellarby et al., 2008). As presented by the European Environment Agency (EEA) 1 , at the European level, GHG emissions from agriculture have declined since 1990. For the 27 countries of the European Union (EU) GHG emissions from agriculture have decreased by 120 MtCO2eq (-‐20%) between 1990 and 2008. GHG emissions from agriculture accounted for 10% of total GHG emissions in 2008 (+0.2% compared to 2007). As in the global assessment, N2O (57%) and CH4 (43%) were the predominant GHGs in agriculture. Half of the emissions derived from microbiological activities in agricultural soils, 30% from enteric fermentation and nearly 20% from manure management. At the national scale, and for the year of 2011, agricultural sector was responsible for about 11% of total national emissions, 7.5 MtCO2eq (excluding LULUCF and international bunkers). The enteric fermentation was responsible for 2.8 MtCO2eq (37%); manure management for 1.3 MtCO2eq (17%); rice cultivation for 0.5 MtCO2eq (7%) and direct soil emission due to the use of synthetic fertilizer for
1
Information available at http://www.eea.europa.eu/themes/agriculture/intro
23
0.6 MtCO2eq (8%) (APA, 2013). In comparison with 1990 there was a decrease in importance and total emissions, which were, respectively, 13.4% and 8.2 MtCO2eq (APA, 2013). Given the above, and as already pointed out by Schlamadinger et al. (2007), there are other ways to reduce net GHG emissions, and therefore, also contributing to the achievement of the UNFCCC’s goals. The proposed ways include bioenergy production, reduction of emissions of non-‐ CO2 gases, and substitution for less carbon intensive products (Schlamadinger et al., 2007). According to the EEA1, although farmers represent only 4.7% of the working population in the EU, they are responsible for the management of about half of the land, with a big influence on landscapes and quality of its environment and not only on the climate change topic. The main resources under pressure are soil, air, water and biodiversity. As for impacts, soil degradation, including soil erosion; water pollution and excessive use; and biodiversity decrease, are among the most discussed1. Soil erosion by water and wind affects almost 15% of EU land, with specific problems concentrated in the Mediterranean and Eastern European region. As for water pollution, agriculture is responsible for a major pressure on the quality of ground and surface water in the EU, particularly relevant on the north-‐western countries. Agricultural nitrogen surplus (the difference between all nutrient inputs and outputs on agricultural land), although positive for all European countries (see Figure 2), show a declining trend, thereby potentially reducing environmental pressures not only in water, but also on soil and air. In Portugal, and for 2011, N balance was about 11 kgN.ha-‐1, also with a declining trend (INE, 2009).
F IGURE 2 – E UROPEAN NITROGEN SURPLUS (EEA AND JRC, 2010)
Environmental problems associated with the excessive use of water include lowered water tables, salinization and damage to terrestrial and aquatic habitats. The Mediterranean area is particularly sensitive due to the occurrence of droughts (Walls, 2006). Europe’s biodiversity is linked to agricultural practices through the creation of agro-‐ecosystems. A large number of highly valuated wildlife species and semi-‐natural habitats types are dependent on continuing low-‐intensity agricultural practices. Of the 231 habitat types of European interest targeted by Annex I of the EU
24
Habitats Directive, 55 depend on extensive agricultural practices or can benefit from them. Similarly, 11 targeted mammal species, 7 butterflies species and 10 orthoptera species, as well as 28 vascular plant species depend on the continuation of extensive agriculture. However, as the agricultural production has intensified, all levels of biological diversity (genetic, species, and habitats) have declined in farming environments1. In addition, the use of farm machinery releases to the atmosphere not only GHG but also other residues with an environmental impact. The production and transport of input factors to the cultivation place, as fertilizers, pesticides, seeds and machinery also implies an indirect environmental impact linked to the agricultural production. The need to include environmental impact categories other than climate change is, therefore, clear, as already exposed by Smith et al. (2009). According to Gerber et al. (2013), “supportive policies, adequate institutional frameworks and more proactive governance (…)” are needed to fulfil the potential of environmental impact reduction. As presented by Baudron and Giller (2014) and Odegard and van der Voet (2014) global demand for agricultural products is expected to double in the next decades pressing the ecosystems to produce more. Policy makers face the challenge to focus on mitigation strategies that serve both development and environmental goals (Gerber et al., 2013). The comparative assessment of environmental impacts from substitute scenarios of cultivated systems facilitates policy makers’ decision towards more sustainable options of production. When developing substitute scenarios, we assumed that land currently used to agricultural production is likely to remain productive and that, at least, the substitute cultivated systems must be conservative in terms of the amount of goods. In order to answer to these requisites both direct land use change (DLUC) and indirect land use change (ILUC) are assessed (see Note 4). N OTE 4 – DLUC AND ILUC DEFINITION
Following the reasoning by Overmars et al. (2011), the area used by a given cultivated system could be productive or unproductive in the absence of that system. The land use change from the current use to the considered cultivated system is called DLUC. In the case of using productive land, the original production will have to be realized elsewhere, either through the conversion of land into agricultural use or through intensification of agriculture to increase yields. This is called ILUC effect.
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1.2.
S T A T E O F T H E A R T & C O N T R IB U T IO N
For the assessment of soil carbon sequestration, as proposed by Post et al. (2001), there are both direct and indirect methods to quantify soil carbon stocks and changes. Direct methods use measurements of C stocks or fluxes to determine changes in soil C amounts. Indirect methods use information from previous studies combined with auxiliary information, such as cartography. Freibauer et al. (2004) presented a collection of values regarding the potential carbon sequestration as a response to management techniques. Smith (2004a) provided an interpretation of graphical representations from Freibauer et al. (2004), with the results shown in Table 1. These values represent the carbon sequestration potentials achievable by 2012, limited only by availability of land, biological resources and land suitability (Smith, 2004a). As discussed by Smith et al. (2009), although the considerable potential, lack of policy incentives jeopardizes its fulfilment. T ABLE 1 – C ARBON SEQUESTRATION POTENTIAL (S MITH , 2004 A ) Management technique Zero tillage Reduced tillage Set-‐aside Permanent crops Deep-‐rooting crops Animal manure Cereal straw Sewage sludge Bioenergy crops Extensification Convert cropland to grassland
Soil carbon sequestration potential -‐1 -‐1 (tCO2.ha .yr ) 1.39 < 1.39 < 1.39 2.27 2.27 1.39 2.53 0.95 2.27 1.98 4.40 – 6.20
Estimated uncertainty (%) >50 >>50 >>50 >>50 >>50 >>50 >>50 >>50 >>50 >>50 >>50
Worldwide there are case studies assessing soil carbon sequestration factors related to particular management techniques. Portugal is not an exception and examples are the analysis of the role of natural grassland biodiversity by Teixeira et al. (2011) and the role of tillage on cropland by Carvalho et al. (2012). The assessment of GHG emissions from cultivated systems has been extensively done in the context of the KP national accountings. As for the analysis of other impact categories, several studies have been developed. Institutions such as the EEA and Joint Research Centre (JRC) regularly publish reports on the state of resources and the influence of agricultural practices. Examples of environmental impact analysis from cultivated systems are the assessment of agri-‐environmental relationships in the EU-‐15 presented by EEA (2005); the 10 main messages regarding the effect of agricultural ecosystems on biodiversity by EEA (2010a); and an overview of the main environmental impacts from agriculture by Walls (2006). As for the analysis of resources, with a link to the role of agricultural practices, soil assessment can be found in EEA and JRC (2010) and Jones et al. (2012); water assessment is presented by Commission (2009), EEA (2009) and OECD (2006); biodiversity assessment can be found in EEA (2010b) and Gardi and Jeffery (2009). Although these studies present quantitative and qualitative results, usually they are not focused on the direct and indirect impact from a particular systems or good. The inclusion of direct and indirect impacts on the analysis of cultivated systems can be found in studies such as the one by Ruviaro et al. (2012) and Roy et al. (2009). The assessment of direct and indirect impacts from alternative production systems, for a given agricultural product, can be found in studies such as the ones by Patil et al.
26
(2014) and Cederberg and Mattsson (2000). The inclusion of DLUC and ILUC is a recommended practice in the bioenergy assessment context. However, its reasoning applies to any productive system, of which Dumortier et al. (2011) is an example. The analysis of both carbon sequestration and other direct and indirect impacts is presented by Gerber et al. (2013). The mentioned study regards livestock production systems, but only assesses the impact in terms of climate change and regardless ILUC. In the present thesis we aim to analyse the environmental impacts from substitute cultivated systems. The designed general approach includes a set of direct and indirect environmental impacts, constant food production as well as direct and indirect land use change effects. The developed approach is applied to Portuguese cultivated systems. Within the Portuguese cultivated area (including temporary crops, permanent crops, domestic production and permanent grassland), the two main occupations are permanent grasslands, with 47% of the area (according to 2009 data), and temporary crops, with 23%2. Regarding the occupation by natural grasslands, it is of particular relevance the fact that Portugal, as a signatory country of the KP, elected the Article 3.4 within the LULUCF sector, regarding grassland management. As presented in Figure 1, Portuguese soil carbon stocks, translated into SOM, are low when compared with the European average, suggesting a relevant potential for carbon sequestration. The first case study addresses this issue, aiming to assess if there is a difference in SOM concentration between tilled and no-‐tilled plots, both in areas occupied by natural grasslands. The two substitute scenarios differ on the shrub management technique, but not the agricultural product, in this case, cattle. This allows us to focus only on the role of the management technique on SOM concentration. In addition, since no tillage management requires more time of operation per ha, we also estimate direct and indirect environmental impacts from mechanical operation, assuming all the remaining input factors constant. In Portugal, and for the year of 2009, irrigated area represented 13% of total used agricultural area (about 22% if pastures are excluded). Between 1989 and 2009, the proportion of irrigated area relative to the irrigable area increased from 72 to 87%2. According to EEA, the increase of irrigation constitutes a severe potential problem. The major identified issues are overexploitation and consequent severe water shortages during dry periods, decrease in water quality and the risk of salt-‐water intrusion into groundwater. The second case study regards the environmental assessment of Portuguese irrigated systems. The analysis is performed in comparison with the most likely substitute system (rainfed production or imports) and covers six agricultural products, namely rice, maize, tomato, broccoli, orange and olive. The need to fulfil the European bioenergy targets has triggered Portuguese regulation to consider electricity generation using energy crops as raw material. However, as already explained in Van Stappen et al. (2011), some concerns have been raised regarding the possibility that bioenergy is actually worse than its fossil equivalent, not only in terms of GHG emissions, but also in a broader environmental perspective. Also, the production of the energy crops may occupy land previously used to the production of agricultural products that have to be produced elsewhere. The third case study considers the environmental impact analysis of dedicated production of eucalyptus to be used as a raw material in electricity co-‐generation, in comparison with the electricity generation 2
Information available at www.ine.pt
27
using exclusively coal as raw material. It is assumed that eucalyptus production takes place in land previously used for temporary crop production, namely maize, tomato and broccoli. The chosen case studies answer to relevant national questions, with impact on the context of policy making. While the Portuguese Carbon Fund3 (PCF) promoted the implementation of a project regarding the control of shrub encroachment at grasslands through the use of no-‐tillage techniques, substituting areas subject to tillage management, the analysis of irrigated production systems is used in European negotiations aiming the promotion of Portuguese agriculture.
3
PCF is an operational instrument that intends to finance initiatives that decrease GHG emissions in Portugal.
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Chapter 2. M ETHODOLOGICAL OVERVIEW 2.1.
O V E R A L L A P P R O A C H
As already discussed, soil management, with correspondent SOC stock change, is presented as an important climate change mitigation strategy (Schlamadinger et al., 2007; UNFCCC, 1992). According to Figure 3, SOC change may occur due to a change in the cultivated system.
SOC$stock$change$
Cul1vated$ system$A$
Agricultural$ product$A$
Subs1tutes$
Subs1tutes$
Cul1vated$ system$B$
Agricultural$ product$B$
F IGURE 3 – SOC STOCK CHANGE DUE TO THE SHIFT IN CULTIVATED SYSTEM
The here designated “Cultivated system A” and “Cultivated system B” may represent: •
a complete change in land use characteristics and in agricultural product, such as from cropland to grassland; or
•
a change in a particular management technique, without changing the main product
(“Agricultural product A” equal to “Agricultural product B”), such as adoption of no-‐ tillage techniques. With this approach only climate change direct impacts from SOC stock change are considered. In general terms, cultivated systems, besides land occupation, requires input factors, such as water, fertilizers, pesticides and machinery (see Figure 4). Its use in the cultivation process implies not only the depletion of resources but also the emission of residues into the global ecosystem, with a correspondent direct environmental impact. The emissions into global ecosystem have an impact not only in terms of climate change, but also in other environmental impact categories. As presented in Figure 4, in order to have the production factors available at the cultivated system site, there was the extraction of raw materials, industrial processing and transport of raw, intermediate and final products, with indirect impacts associated.
29
Water' Fer3lizers' Raw'material' extrac3on' Processing' Transporta3on'
Pes3cides'
Cul3vated' system''
Agricultural' product'
Machinery' …'
Emissions'into'the'global'ecosystem'
F IGURE 4 – C ULTIVATED SYSTEMS AS A SOURCE OF EMISSIONS INTO THE GLOBAL ECOSYSTEM
The analysis of direct and indirect environmental impacts is performed considering a life cycle assessment (LCA). As explained in ISO 14040, LCA allows the analysis of environmental impacts of products (goods or services), from raw material acquisition to final disposal. This is done in a systematic way in accordance with the stated goal and scope, and including direct and indirect impacts. Quoting Schmidt (2008) “Agricultural LCAs typically regard local production as affected and they only include the interventions related to the harvested area. However, as changes in demand and production may affect foreign production, yields and the displacement of other crops in regions where the agricultural area is constrained, there is a need for incorporating the actual affected processes in agricultural consequential LCA”. Examples of consequential LCA applied to agricultural systems are the studies by Schmidt (2008), Dalgaard et al. (2008) and Cederberg and Stadig (2003). As defined by Ekvall and Weidema (2004), a consequential LCA is designed to generate information on the consequences of decisions. LCA can, therefore, be very useful in the policy making context through the comparison of substitute scenarios for the production of a given good or service (Jensen et al., 1997; Tillman, 2000). This is precisely our approach, aiming to evaluate the best scenario of production considering the analysed environmental impact categories. By assuming both inelastic demand for agricultural goods and that productive land remain in production, we include the effects of DLUC and ILUC. The overall approach is presented in Figure 5. As schematized in Figure 5, a given cultivated system A in area I allows the production of A. As an alternative, area I can be used to produce product B (for example, if A regards a new product, then it will occupy area previously used to the production of B). Therefore, in order to maintain the production of B constant, there is the need to produce it elsewhere. We are often interested in compare alternatives to the production of A, and therefore, if area I is allocated to the production of B, then area II may be used to the production of A. Summing up, if A is produced in area I, then B is produced in area II; otherwise, if B is produced in area I, then A is produced in area II. Both scenarios include a constant production of A and B. All cultivated systems require resources and releases emission into the global ecosystem, with correspondent direct and indirect environmental impacts.
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Resources*consump4on* Agricultural* land*[ha]*
Agricultural* land*[ha]* Resources*consump4on*
Resources*consump4on* Agricultural* land*[ha]*
Agricultural* land*[ha]* Resources*consump4on*
Cul4vated* system*A*in* *area*I*
Cul4vated* system*B*in* *area*I*
Cul4vated* system*B’*in* area*II*
Cul4vated* system*A’*in* area*II*
Emissions* Agricultural* product*A*[t]*
Emissions* Agricultural* product*B*[t]*
Agricultural* product*B*[t]* Emissions*
Agricultural* product*A*[t]* Emissions*
F IGURE 5 -‐ O VERALL METHODOLOGICAL APPROACH
The simplification of Figure 5 to reflect the first case study is presented in Figure 6. In this case we are interested in the analysis of substitutes to the service “shrubland control in 1 ha of natural grasslands”. Here, the only difference between “Cultivated system A” and “Cultivated system B” is the shrub management control technique. Since the stocking rate is assumed constant, “Agricultural product A” is equal to “Agricultural product B” and therefore, no ILUC is necessary. Given the expected importance of soil management in SOC change, this subject is here analysed in detail. Also, it is considered that systems differ on the machinery hours per ha, with the correspondent direct and indirect environmental impacts estimated through a LCA. Resources&consump=on& Agricultural& land&[1&ha]&
Agricultural& land&[1&ha]& Resources&consump=on&
Natural&grasslands&in& Portugal& &(Tillage&management)&
Natural&grasslands&in& Portugal& &(No 2mm) content should be estimated and subtracted from the soil volume, with special attention to non-‐agricultural soils which are often rockier and less homogeneous (Post et al., 2001). In its estimation, one common method is by excavation. For this method, a soil monolith with a predefined side length is extracted from the soil and the excavated volume has to be determined accurately together with the stone content within the monolith. An alternative to the volume determination is to try to excavate an exact monolith with exact side length and right angles. This method is very laborious, destructive and time consuming (although it may be the only option, especially in stony soils). Other possibilities include the “rod penetration method” and the use of visual estimates. The first one has only been calibrated for glacial till soils and its application in other geographical areas requires evaluation; the second is very subjective, therefore good validation training is essential (Rodeghiero et al., 2009). A study for arable soils by Rytter (2012) shows that, at most, the summed stone and gravel fraction were 7.7% of the investigated soil volume. Arable soils, given their agricultural use, are a good estimate of the lower stoniness level. An upper bound can be given for forest samples, with values as high as around 50% (Rytter, 2012). Root biomass (living parts only, as dead roots are part of the SOM) can represent a large portion (e.g. 10 to 40%) of the total biomass of an ecosystem. Usually, the determination of root biomass is carried out by either (Rodeghiero et al., 2009): •
applying analytical functions, linking root biomass to more easily measurable biomass variables (such as root-‐to-‐shoot ratios or stem diameter);
•
sampling root biomass from soil excavation pits or soil corers.
The choice of the method will depend mostly on economic constraints, since the sampling with both methods is quite difficult and time consuming. Application of analytical functions is quite limited as they are usually species, site and age specific.
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2.3.
C A R B O N & B E Y O N D
As presented before, in order to assess both direct and indirect impacts, together with a broader scope of environmental categories, a LCA is considered. The analysis is performed using software SimaPro 7.2, developed by Pré Consultants of the Netherlands (http://www.pre.nl/) and the National Reuse of Waste Research Programme. As summarized by Rebitzer et al. (2004), and in accordance with the ISO 14040, LCA is composed of 4 phases, that interact as presented in Figure 10. Goal%and%scope% defini.on%
Inventory% analysis%
Interpreta.on%
Impact% assessment% %
F IGURE 10 – P HASES OF A LCA AS PRESENTED BY R EBITZER ET AL . (2004)
In the goal and scope definition it is aimed to describe the product system in terms of boundaries and functional unit (Rebitzer et al., 2004). Starting here and all along the way interpretation of the intermediate results must be considered. The next phase, life cycle inventory (LCI) is a “methodology for estimating the consumption of resources and the quantities of waste, flows and emissions caused by or otherwise attributable to a product’s life cycle” (Rebitzer et al., 2004). Life cycle impact assessment (LCIA) “provides indicators and the basis for analysing the potential contributions of the resources extraction and waste/emissions in an inventory to a number of potential impacts” (Rebitzer et al., 2004). The result of the LCIA is an evaluation of a product life cycle in terms of several impacts categories and, in some cases, in an aggregated way (such as years of human life lost due a given impact). As presented by Pennington et al. (2004), there are both mandatory and optional elements in the process of LCIA (see Figure 11). Associated with the defined goal and scope, there is the need to select impact categories of interest. The inventory data must then be assigned to the chosen impact category (classification phase). The calculation of impact categories is done through the use of characterisation factors (characterisation phase). These are the mandatory elements. After them, is possible to recalculate the category indicator result relative to reference values (normalisation phase). The impact categories can also be grouped and/or weighted.
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Mandatory)elements) Selec%on(of(impact(categories,(category(indicators,( characterisa%on(models( ( ( Assignment(of(LCI(results( ( ( Calcula%on(of(category(indicator(results(
Category(indicator(result((
Op0onal)elements) Calcula%on(of(the(magnitude(of(category(indicator(rela%ve(to( reference(informa%on( ( ( Grouping( ( ( Weigh%ng(
F IGURE 11 – E LEMENTS OF LCIA AS PRESENTED BY P ENNINGTON ET AL . (2004)
In the present thesis, only the mandatory elements are considered. By excluding normalization and weighting, it is aimed to reduce subjectivity (Geodkoop, 1995). The impact assessment methods considered is the ReCiPe (Goedkoop et al., 2012). According to the explained only midpoint results are considered. Given its consensually, the hierarchist perspective is assumed. If is not possible to apply a LCA, we consider data available in literature.
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Chapter 3. C AN NO -‐ TILLAGE CONTRIBUTE TO SOIL CARBON SEQUESTRATION IN PORTUGUESE NATURAL GRASSLANDS ? Context Under the Article 3.4 of KP, in the framework of voluntary LULUCF activities, Portugal elected the activities “Grassland Management”, “Cropland Management” and “Forest Management”. However, even using such additional measures, the 2006 National Program on Climate Change pointed to excess in emissions. As presented in detail by Teixeira (2010), the PCF was created to fund and promote projects with innovative approaches, aiming to increase carbon sequestration through LULUCF activities. Within this context, Terraprima -‐ Environmental Services, a spin-‐off enterprise from Instituto Superior Técnico, developed a project regarding the control of shrub encroachment at grasslands through the use of no-‐tillage techniques, substituting areas subject to tillage management. The correspondent carbon sequestration is paid to farmers as an ecosystem service. The present chapter regards the estimate of carbon sequestration factors. Previous versions of this work were already presented and/or published, namely: •
Oral presentation entitled “Shrubland management as a tool to sequester carbon” at the III National Congress on Climate Change, 1-‐2 June 2012, Caparica, Portugal.
•
Oral presentation entitled “The role of grassland and shrubland management in soil carbon sequestration” (O papel da gestão de pastagens e matos no sequestro de carbono no solo) at the V Iberian Congress of Soil Science (V Congresso Ibérico da Ciência do Solo), 6-‐10 September 2012, Angra do Heroísmo, Portugal.
•
Oral presentation entitled “Grazed landscapes: the importance of land management” at the Cascais World Forum 2012 -‐ Soil Bioengineering and Land Management New Challenges, 19-‐22 September 2012, Cascais, Portugal.
•
Oral presentation entitled “Grassland management options under Kyoto Protocol Article 3.4 – the Portuguese case study” at the 14th Meeting of the FAO-‐CIHEAM Subnetwork on Mediterranean Pastures and Fodder Crops: "New Approaches for Grassland Research in a Context of Climate and Socio-‐Economic Changes”, 3-‐6 October 2012, Samsun, Turkey. There was a correspondent publication with international peer review at the Options Méditerranéennes, A, no. 102, 53-‐56.
•
Oral presentation entitled “The impact of tillage versus no-‐tillage on soil organic matter: the case of shrub management at Portugal” (O impacto da gestão por gradagem versus corta-‐mato no teor de matéria orgânica do solo: o caso da gestão de áreas de mato em Portugal) at the Annual Meeting of the Soil Science by the Portuguese Society of Soil Science (Encontro Anual da Ciência do Solo / SPCS 2013), 26-‐28 June 2013, Oeiras, Portugal.
•
Oral presentation entitled “The role of grassland management in the climate change” at the 10th National Environment Conference and XII National Congress of Environmental Engineering (10.ª Conferência Nacional do Ambiente e XII Congresso Nacional de Engenharia do Ambiente), 6-‐8 November 2013, Aveiro, Portugal.
The presented work counted with Manuel Ribeiro and Helena Martins as co-‐authors.
39
The chapter in a nutshell Tilling soil is a common practice in shrub encroachment control in Portuguese natural grasslands. However, tilling the soil is disruptive and leads to SOM loss. In this study, we developed a sampling design including 145 plots (81 subject to tillage management and 64 subject to no tillage management), collected in 2011 and 2012. Our main goal is to assess if there is a difference in SOM averages between tilled and no-‐tilled plots. Parametric and non-‐parametric mean test hypothesis, as well as multiple linear regression, is considered in the analysis. The results indicate that the null hypothesis of equal SOM average among management techniques is rejected with a 95% confidence interval (CI), indicating higher SOM concentration for data regarding no-‐tilled plots. The obtained values regarding the difference between management techniques range from 0.02 to 0.31 gSOM/gsoil.yr-‐1 for 2011 and 0.01 to 0.25 gSOM/gsoil.yr-‐1 for 2012. Since 58% of SOM is SOC, it is possible to estimate the carbon sequestration. For 2011 the obtained carbon sequestration factor range from 0.96 to 17.16 tCO2.ha-‐1yr-‐1. For 2012, the carbon sequestration factor range from 0.52 to 13.71 tCO2.ha-‐1yr-‐1. Regarding critical analysis of the results, the estimate of bulk density, the sampling depth and the SOM laboratory analysis were identified as key issues in the presented analysis.
3.1.
I N T R O D U C T IO N
Historically, natural grasslands are the most common type of grasslands in Portugal. These grasslands are located in regions with high risk of desertification and, as described in Teixeira et al. (2011), subject to shrub control through the use of tillage. However, and as pointed out by Conant et al. (2007), soil tillage is disruptive of soil structure, since it can promote soil erosion and depletion of SOM. Here, we focus our analysis on SOM since it plays a crucial role in maintaining soil functions (due to its influence on soil structure and stability, water retention, soil biodiversity and as a source of plant nutrients) (EEA and JRC, 2010). As explained by Bot and Benites (2005), tillage activities decrease SOM concentration by increasing mineralization rates and decreasing humus production. Tillage increases SOM mineralization rate since it stimulates the heterotrophic microbiological activity through soil aeration. Through breakdown of soil structure, it also decreases upward and downward movements of soil fauna, such as earthworms, which are largely responsible for “humus” production. On literature no-‐tillage practices have then been presented as an option to stop SOM decline or even recover SOM lost during tillage (Blanco-‐Canqui and Lal, 2008; Conant et al., 2007; Paustian et al., 1997; Smith, 2008). According to Bot and Benites (2005), no-‐tillage regulates heterotrophic microbiological activity, facilitates the activity of the “humifiers” and allows soil fauna to resume their bioturbating activities. In this case, the action of soil macrofauna gradually incorporates the biomass from the soil surface down into the soil. Since part of SOM is SOC (Post et al., 2001), when promoting an increase in SOM there is a correspondent carbon sequestration. The increase of SOC through the pathway of atmospheric CO2 sequestration offsets emissions from other sources (Conant et al., 2010) and thus falls within application range of the KP (Smith et al., 2009). As previously stated, Portugal elected the voluntary activity “Grassland management”, in the framework of Article 3.4 of the KP. Assuming the hypothesis that the shift from tillage to no
40
tillage in Portuguese natural grasslands allows an increase in SOM levels, a proposal of project was made to the PCF. In this context, the main goal here is to test if the shift in shrub control from tillage to no-‐ tillage is a relevant measure to improve SOM concentration in Portuguese natural grassland. If that is the case, estimate the correspondent carbon sequestration. In order to do so, 145 plots were sampled. Samples were collected in 2011 and 2012. Although SOM levels are influenced by many factors, we here analyse the effect of soil original material. The used method consists of a battery of statistical tests and Stata software is used. Since the assumption proved to be valid, FPC supported the project, which is currently implemented in about 47 000 ha, involving 191 farmers. Regarding the state of the art, worldwide studies have been done regarding the carbon sequestration due to reduce or no tillage. While some, like the one by Young et al. (2009), VandenBygaart et al. (2008), Luo et al. (2010) and Loke et al. (2012) show little or zero carbon sequestration others, like the ones by Sanderman et al. (2010), West and Post (2002) and Boddey et al. (2010) show carbon sequestration rates with a upper bound of 5.6 tCO2.ha-‐1.yr-‐1. As for the analysis of carbon sequestration by grasslands, the role of Portuguese natural and sown biodiverse grasslands has already explored by Teixeira et al. (2011). A 10 years model of SOM dynamic was obtained with an average carbon sequestration of about 2.6 tCO2.ha-‐1.yr-‐1 and 6.5 tCO2.ha-‐1.yr-‐1 for natural and sown biodiverse grasslands, respectively. A higher factor of sequestration, 7 tCO2.ha-‐ 1 .yr-‐1, is reported by Aires et al. (2008). The study regards grazed Mediterranean C3/C4 grassland in southern Portugal, during two hydrological years (2004-‐2005 and 2005-‐2006). Considering global temperate grasslands Conant et al. (2001) reports a range from 1.10 to 11.50 tCO2.ha-‐1.yr-‐1. In Table 3 we present a more extensive review of literature.
41
T ABLE 3 – M EASURED GRASSLAND CARBON SEQUESTRATION FACTORS Soil carbon potential -‐1 -‐1 (tCO2 ha yr )
Land use / land use change -‐1
Semi-‐natural calcareous grasslands (2 cuts yr ) -‐1
Sown Trifolium repens (4 cuts yr ) -‐1 Sowm Lolium perenne (4 cuts yr ) -‐1
Sowm Trifolium repens (4 cuts yr ) -‐1
Sowm Lolium perenne (10 cuts yr ; High N, -‐1 -‐1 800kg.ha yr ) -‐1 Sowm Lolium perenne (5 cuts yr ;Low N, LN, -‐1 -‐1 -‐1 -‐1 160 kgha yr ;High N, HN, 530 kgha yr ) Species-‐rich grassland on limestone soil in -‐1 solardome (4-‐5 cuts yr ) Species-‐poor community on peaty gley in solardome (No cutting/grazing) Annual grassland on serpentine (No cutting/grazing) Annual grassland on sandstone (No cutting/grazing)
< 2.93 8.80 – 24.93 9.90 – 36.30 6.97 11.37 8.40 14.30 2.20 (LN), 2.93 (HN) 5.13 (LN), 6.97 (HN) 2.57 – 6.23
Method
Climate
C labelling in open top chambers (OTC) 13 C labelling in free air CO2 enrichment (FACE) 13
C-‐enriched soil (C4) in FACE C balance Soil C analysis
C balance
21.27 – 25.67 31.17 – 32.63 13
C labelling in OTC
600 ppm CO2
Niklaus et al. (2001)
600 ppm CO2
Switzerland
Ambient CO2 600 ppm CO2 Ambient CO2 2xambient CO2 Ambient CO2 2xambient CO2 Ambient CO2 Ambient CO2 + 250ppm CO2 Ambient CO2 Ambient CO2 + 250ppm CO2
Van Kessel et al. (2000) Nitschelm et al. (1997)
Netherlands
Schapendonk et al. (1997)
France
Loiseau and Soussana (1999)
UK
Fitter et al. (1997)
2xambient CO2
USA (California)
Soil C analysis
USA (Central Texas)
CESAR model
Commitment period (2008-‐2012)
Europe
Hungate et al. (1995)
9.64 – 12.58
Perennial grassland (from arable to grassland)
1.65
European grasslands (average) European grasslands (conversion of all arable land to grassland) European grasslands (application of farmyard manure)
1.91 5.28 5.50
42
Reference
13
8.43 – 10.63
7.55 – 11.95
Location
Potter et al. (1999)
Vleeshouwers and Verhagen (2002)
Land use / land use change Managed grasslands (reduction in N fertilizer inputs in intensive leys) Managed grasslands (conversion of arable land to grass/legume) Managed grasslands (intensification of permanent grassland) Managed grasslands (intensification of nutrient-‐poor grassland) Managed grasslands (permanent grassland to medium-‐duration leys) Managed grasslands (increasing duration of leys) Managed grasslands (short duration leys to permanent grassland)
Soil carbon potential -‐1 -‐1 (tCO2 ha yr )
Method
Climate
Location
Simple statistical model
Ambient
France
Soussana et al. (2004)
Global
Parton et al. (1995)
Global
Parton et al. (1995)
1.10 1.10 – 1.83 0.73 -‐3.30 – 4.03 -‐0.73 0.73 – 1.83 1.10 – 1.47 0.01
Temperate steppe (C3/C4, unfertilized)
0.11
CENTURY model
-‐0.11 -‐0.11 -‐0.11
Humid temperate (C4)
0.11 -‐0.22
CENTURY model
-‐0.10 -‐0.20 -‐0.11
Reference
350 ppm CO2 +Climate Change (CC) 2 x ambient CO2+CC +CC 2 x ambient CO2+CC 350 ppm CO2 +CC 2 x ambient CO2+CC +CC 2 x ambient CO2+CC
43
Land use / land use change -‐1
-‐1
Temperate grassland (N input, 20 kg ha yr for the upland site, US; 50 for the lowland site, LS)
Soil carbon potential -‐1 -‐1 (tCO2 ha yr )
Hurley pasture model
0.33 0.55
Temperate grasslands (fertilized) Temperate grasslands (improved grazing) Temperate grasslands (conversion from arable to permanent grassland) Temperate grasslands (conversion from native vegetation to grassland) Temperate grasslands (introduction of legumes) Temperate grasslands (earthworm introduction) Temperate grasslands (improved grass species) European grasslands (conversion from arable land to 1:3 yr ley: arable rotation) Cool temperate grasslands (conversion from arable land to permanent pasture) Rangeland (poorly managed) Rangeland (well managed) Rangeland (conservation reserve program)
44
Climate
Location
Reference
UK
Thornley and Cannell (1997)
Standard conditions, US
0.29 0.18
Temperate grassland (range from low, 5, to -‐1 -‐1 kigh, 100 kgNha yr atmospheric fixed N flux)
Method
1.10 1.28
Standard conditions, LS +5°C + 700 ppm CO2, US +5°C + 700 ppm CO2, LS
3.70 1.28 -‐
Ambient
Global
Conant et al. (2001)
Europe
Smith et al. (1997)
2.75 8.62 11.15 5.87 0.99 0.37 1.1 2.2
USA
Post and Kwon (2000)
USA
Schuman et al. (2002)
3.2.
M E T H O D S
3 .2 .1 S A M P L I N G D E S I G N As stated before, our goal is to assess if the shift in shrub control from tillage to no-‐tillage is a relevant measure to improve SOM concentration in Portuguese natural grasslands. In terms of project implementation, as approved by the PCF, the goal is to obtain a sequestration factor as applicable to the national level as possible. Here, we approach the problem with direct methods, through measurement of C stocks in designated plots, and extrapolation to represent a given geographic area. The analysis of SOC content is a standard laboratory procedure but SOC stocks vary as a function of soil texture, landscape position, drainage, and soil density, all of which vary spatially, making it difficult to quantify changes in SOC stocks (Conant et al., 2010). Therefore, the main difficulty in documenting plot level changes in SOC stocks is not with measuring the SOC, but rather in designing an efficient, cost effective sampling and SOC stock estimation system (Post et al., 2001). The development of scaling procedures to relate soil organic C changes on individual plots or fields to a regional or national accounting is also a challenge. The fundamental method for scaling consists of subdividing the landscape into relatively homogeneous patches, applying field measurements or model results for each subdivision (Post et al., 2001). Here we considered the subdivision in soil original material. Although the detection of SOM changes is most commonly achieved by the use of time series, we here assume a space-‐for-‐time substitution approach. Space-‐for-‐time substitution regards analyses in which contemporary spatial phenomena are used to understand and model temporal processes (Blois et al., 2013). Since it is very difficult to adequately detect a small change in SOC stocks over time periods of less than 5 years (VandenBygaart and Angers, 2006) the proposed methodology allow us to obtain useful results in a time effective way. However, “space-‐for-‐time” substitution approach, while useful, can produce erroneous results if soil characteristics differ among sites. Selecting sites with similar parent material, soil type, landscape position and prior land use history can minimize sources of error (Lal, 2005). Following Schrumpf et al. (2008), we set up a regular grid sampling protocol. Grid designs are often easier to handle than random sampling, since the points are easier to locate in the field, for the first inventory as well as for the re-‐sampling (Schrumpf et al., 2008). The grid was chosen considering the need to harmonise the current procedure with the one from the Portuguese National Forestry Inventory (NFI). This harmonisation aims the possibility of future use of both databases in a compatible way. Within the NFI grid, sampled plots were chosen, taking into consideration the representativeness of data points for both shrub control techniques and soil original materials (see Table 4). The sampled plots are not experimental and controlled. Instead, there are under current management, following practical needs. The choice of plots started with an interview to the farmer aiming to understand both the current management and the availability to participate in the study. Our goal was to find a set of plots as balanced as possible regarding the control management and soil original material. As much as possible, the history of plots was gathered. On one hand this design allows us to have a glimpse of what is, in fact, the current status of soil, on the other hand it does not allow the knowledge and control of management techniques.
45
This implies the ignorance about the details regarding mobilization execution (depth and exact machinery) and exact date of occurrence. We were able to collect soil samples from 145 plots, 23 different farms and 10 municipalities of mainland Portugal (see Table 5 and Figure 12). The samples were collected in 2011 and 2012 between July and November (before the heavy rain season). Due to budget and human resources constraints was not possible to shorten the time frame. This time frame included differences in climatic conditions, which may have influenced SOM concentration and compromise results. The annual collection of data continues, aiming to collect a time frame long enough to model SOM evolution with time. T ABLE 4 – S AMPLING EFFORT BY MANAGEMENT TECHNIQUE AND ROCK MATERIAL Soil original material All Granite Sandstone Schist
Management No-‐till Till No-‐till Till No-‐till Till No-‐till Till
Number of samples 2011 2012 64 63 81 82 6 5 29 30 26 26 41 41 32 32 11 11
T ABLE 5 – D ISTRIBUTION OF SAMPLED PLOTS BY FARM AND MUNICIPALITY Farm Barbas Barradas da Serra Biscaínho Cascavel Corte Salva Barrocal Estreito Fidalgos Vale Figueira de Baixo Monte Branco Monte Fidalgo Pedras Alvas Quinta da Corona Quinta Grande Santa Cruz e Besteiros Texugueira Torre do Ferrador Torrinha Vale Boi Vale Cão Vale Covo Vale das Porcas Vale Feitoso
46
Municipality Coruche Grândola Coruche Coruche Grândola Évora Grândola Coruche Montemor-‐o-‐Novo Coruche Castelo Branco Grândola Santiago do Cacém Coruche Mora Coruche Coruche Coruche Coruche Alcácer do Sal Coruche Alcácer do Sal Idanha-‐a-‐Nova/Penamacor
Number of samples 1 7 2 3 1 3 3 9 14 4 15 1 5 3 5 4 2 4 3 8 4 18 26
F IGURE 12 – M AP OF P ORTUGAL , WITH THE INDICATION OF THE SAMPLING SITES
According to the NFI guidelines, sampling plots were circular, with an area of about 500 m2, reported to the horizontal plane. The plot thus defined was assumed to be homogeneous regarding the variables of interest. Each soil sample collected in each plot is a composite mixture of 5 sub-‐ samples (one at the centre, and the remaining four collected in the direction of the main 4 cardinal points). Sub-‐samples were collected with a metal probe and afterwards homogenised and labelled (check Figure 13). Major roots or other residues were removed. As recommended by Stolbovoy et al. (2007), the sampled soil depth should be 30 cm. However the sampled soils are shallow and in order to guarantee comparability a 20 cm depth was chosen. The soil samples were sent to the Agricultural Chemical Laboratory of the University of Évora, where they were analysed for their content in organic carbon using a SC-‐144DR (Leco) device. Although the focus of the study is on the relation between the control technique and SOM concentration, other variables were collected. We aimed to gather information that might help explain SOM concentration. Although in the present study those variables play a minor role, as the database grows more mature, they may play a more relevant role in modelling. The collection took place in a cost-‐time-‐effective way, with the possible accuracy. Variables obtained by laboratory analysis of soil samples are presented in Table 6. Variables collected in the field (see Figure 14 and Annex I) are presented in Table 7. In the office, the coordinates of each central point were crossed with the georeferenced data and the variables described in Table 8 were added. The collected data is presented in Annex III.
47
F IGURE 13 – F IELD WORK
F IGURE 14 – F IELD FORM
48
T ABLE 6 – D ESCRIPTION OF “ LABORATORY VARIABLES ” Variable
Type
Unit/scale
Description 4
Determined using a SC-‐144DR (Leco) device . This device can perform gSOM/100gsoil direct combustion and infrared detection for sulphur and carbon in various organic materials from ppm levels to high percent concentrations.
SOM
Ratio
Phosphorus
Ratio
mg/Kg P
Potassium
Ratio
mg/Kg K2O
Acidity
Interval
Manual Texture
Nominal
The extraction was performed according to the Riehm (1958). For the determination of assimilable phosphorus the method from Knudsen (1980) was used. Regarding the assimilable potassium, the method used was flame photometry, with the calibration procedure from the 5 manufacturer (JENWAY PFP7)
pH
Determined through a potentiometric method. Solution soil: water (1:2.5)
-‐
The manual texture assessment is based on the clear different to the touch between sand, silt and clay. It’s a fast and easy method. Procedure: -‐take a piece of land and put it in your palm -‐pinch and rub between your thumb and fingers in order to verify whether it is rough or smooth -‐add water dropwise, and try to shape the land while assessing the characteristics it presents to touch. Classification of texture: COARSE -‐ presents almost exclusively rough materials, is loose, not plastic or sticky and can not shape up in filament. MEDIUM -‐ has a higher proportion of soft materials that harsh, not too sticky, it shaped up into filament and breaks when you want to bend it. FINE -‐ has soft and thin, silky, sticky and easily fashioned into elongated filament which may do not break when bended.
T ABLE 7 – D ESCRIPTION OF “ FIELD VARIABLES ” Variable Forest type Tree cover Shrub cover Herbaceous cover
Percentage Height Percentage Height Percentage
Type
Unit
Description
Nominal
-‐
Estimated by the technician, regards the dominant tree cover.
Ratio
% m % m %
The technician estimates both variables. In order to estimate the percentage, the scheme from Annex II is used. Data collected for 2 the 500 m plot.
Ratio Ratio
Litter
Ordinal
-‐
Estimated by the technician, regards the amount of accumulated litter. It is divided in 5 classes: 1 (absent); 2 (sparse); 3 (medium); 4 (abundant) and 5 (very abundant). Data collected for the 2 500m plot.
Topography
Nominal
-‐
Estimated by the technician, regards the distinction between slope, base, top, plan. Data collected for each sub-‐sample and 2 then homogenized for the 500 m plot.
-‐
The dominant aspect is determined with the compass, from the central point of the plot, with the technician back up the slope. The main eight cardinal points are considered. Data collected for 2 each sub-‐sample and then homogenized for the 500 m plot.
-‐
Estimated by the technician, evaluates if each sub-‐sample is inside or outside the vertical projection of the canopy. Data collected for each sub-‐sample and then homogenized for the 500 2 m plot. Six categories are considered: 0 (5 sub-‐samples inside); 0.2 (4 sub-‐samples are inside); 0.4 (3 sub-‐samples inside); 0.6 (2 sub-‐samples inside); 0.8 (1 sub-‐samples inside); 1 (0 sub-‐samples inside)
Aspect
Canopy
Nominal
Ordinal
4
For more information please check http://uk.leco-‐europe.com/product/sc144dr/ For more information please check http://www.jenway.com/adminimages/pfp7(1).pdf
5
49
T ABLE 8 – D ESCRIPTION OF “ OFFICE VARIABLES ” Variable T_REF_BULK_ DENSITY
Source Harmonized World Soil Database (HWSDB)
Type
Unit 3
Ratio
g/cm
T_BULK_DENS ITY
HWSDB
Ratio
g/cm
Altimetry
Portuguese Environment Agency
Ratio
m
3
Topsoil bulk density, derived from the available analysed data (FAO et al., 2012). -‐
a
P_Evap.
Rosário (2004)
Ratio
mm
Annual potential evapotranspiration.
a
Prec._AY
Rosário (2004)
Ratio
mm
Precipitation level for an average year.
a
Slope
Rosário (2004)
Ordinal
%
Plot slope. Four classes are considered: 1 (0-‐2%); 2 (3-‐ 15%); 3 (16-‐24%); 4 (>25%).
a
Drainage
Rosário (2004)
Ordinal
-‐
Soil drainage. Four classes are assumed: 1 (good to moderate); 2 (imperfect); 3 (bad) and 4 (very bad).
a
Thickness
Rosário (2004)
Ordinal
cm
Soil thickness. Divided in four classes: 1 (>75); 2 (50-‐75); 3 (25-‐50); 4 (6.0); 2 (3.0-‐6.0); 3 (1.5-‐3.0); 4 (0.5-‐1.5); 5 (0.1-‐0.5); 6 (1.5/>0.1 ). b
Data made available by Eng. Lúcio do Rosário, to whom I kindly thank. superficial k/sub superficial k
3 .2 .2 S T A T I S T I C A L A N A L Y S I S O F S O M D I F F E R E N C E S Our goal is to analyse and compare SOM concentration from two datasets, namely (1) tillage plots and (2) no-‐tillage plots (including or excluding differentiation of the soil original material). The statistical analysis is performed with software Stata 11. 3 . 2 . 2 . 1 T W O S A M P L E M E A N H Y P O T H E S I S A P P R O A C H
We start by assessing if there is a statistical significant difference in SOM concentration between the two defined datasets. We assume spatial independence among samples and that the average is representative of the SOM concentration for each dataset. To test the significance of the differences between SOM averages, both parametric (t-‐test) and non-‐parametric (Kruskal-‐Wallis) methods are applied (Zar, 2010). The two methods are compared, aiming to assess the robustness of results. The normality of the SOM data distribution is tested through the Shapiro-‐Wilk test; for intuition, we display a graphical assessment of cumulative frequencies. The homoscedasticity of SOM data is tested using Bartlett’s test (Zar, 2010). For the cases in which it is possible to find a statistically significant difference in SOM concentration between datasets representing soil management systems (no-‐tillage, NT; and tillage, T), lower (L), average (A) and higher (H) SOM differences (ΔSOM) are assessed. The SOM concentrations from t-‐test results, namely upper bound (UB), lower bound (LB) and average (AV), are considered to estimate the SOM concentration difference between systems (see Equation 3).
50
∆𝑆𝑂𝑀! = 𝑆𝑂𝑀!";!" − 𝑆𝑂𝑀!;!" ∆𝑆𝑂𝑀! = 𝑆𝑂𝑀!";!" − 𝑆𝑂𝑀!;!" ∆𝑆𝑂𝑀! = 𝑆𝑂𝑀!";!" − 𝑆𝑂𝑀!;!" E QUATION 3
The analysed data corresponds to a snapshot, as opposed to a time series. We present estimates per year by calculating the number of years needed to achieve the reported SOM difference. In order to do so, the farmers reported the year of last intervention. For the plots under tillage management, the answer is naturally the last year in which a tillage event took place. For the no-‐tillage plots, the farmers tend to give information about last time an intervention took place, not necessarily, and likely, tillage. Therefore, there is the need to estimate the last year of tillage mobilization for the no-‐tillage plots. Using the reported information by farmers, we built an histogram of the reported years passed since the last tillage event (YSLM). According to Figure 15, the cycle of control is of about 7 years. Given the uncertainty, the interval of 7±2 is considered. Three variables were thus defined, namely YSLM5, YSLM7, YSLM9, with the characteristics presented in Table 9. For no-‐tilled plots we assumed that the last tillage event occurred in the last cycle, and 5, 7 and 9 years are subtracted to the year of mobilization reported by farmers. For the assessment of SOM difference in a yearly basis, ∆𝑆𝑂𝑀! considers the average for YSLM9; ∆𝑆𝑂𝑀! considers the average for YSLM7; ∆𝑆𝑂𝑀! considers the average for YSLM5.
F IGURE 15 – H ISTOGRAM OF THE YEARS PASSED SINCE LAST MOBILIZATION FOR THE NO -‐ TILLED PLOTS T ABLE 9 – M EAN AND STANDARD DEVIATION STATISTICS OF YSLM VARIABLES , PER YEAR Variable YSLM5 YSLM7 YSLM9
Average 5.53 6.34 7.16
YSLM 2011 Standard deviation Average 4.10 6.46 4.76 7.26 5.51 8.06
2012 Standard deviation 4.14 4.80 5.56
51
3.2.2.2
M U L T I P L E L I N E A R R E G R E S S I O N A P P R O A C H
Given that samples were collected in different regions, it is not possible to assume the homogeneity of relevant characteristics between observations. This implies that other characteristics, besides the soil management system, may be influencing the difference in SOM concentrations. As for the methodology of homogeneity assessment, for ratio/interval data, the procedure is similar to the one presented for SOM analysis (t-‐test and Kruskal-‐Wallis). For ordinal and nominal data only non-‐parametric methods are applicable. In this case, we are not testing the differences in means but in other measures of distribution (such as the median or frequency). For the ordinal data the Mann–Whitney test is applied; for the nominal data, contingency tables are considered. By considering a multiple linear regression approach, it is possible to measure the influence of tillage/no tillage techniques on SOM concentration while controlling for the effects of other variables. According to the general model presented in Equation 4, the SOM level depends both on the year that the last tillage mobilization took place and other set of relevant variables that may influence the SOM differences. The set of three YSLM variables is also included in the model. The models were developed through a stepwise regression approach in which we assessed the following topics: •
relation between the predicted and observed values through its graphical representation;
•
homoscedasticity of residuals through graphical representation of residuals versus fitted values;
•
existence of omitted variables through the Ramsey test;
•
multicollinearity of independent variables through the variance inflation factor;
•
normality of residuals through the Shapiro-‐Wilk test as well as graphical assessment. 𝑆𝑂𝑀 = 𝛼 + 𝛽! 𝑌𝑆𝐿𝑀 + 𝛽! 𝑋! + 𝜀 E QUATION 4
The difference in SOM concentration due to the management technique is estimated from the 𝛽! coefficient (in units of gSOM/100gsoil.yr-‐1). This method is inspired by work from Conant et al. (2007). Without tillage management, the accumulation of SOM follows an exponential distribution with a level of saturation (Teixeira et al., 2011). We assumed that the plots sampled are far from reaching saturation levels and as such SOM accumulation is approximately linear. According to Figure 16, the total collected data behaves as expected; the SOM levels increase with the years passed since the last mobilization (slope of 0.08±0.03, with a p-‐value of 0.00). For the no-‐tillage data sub-‐set one could assume that SOM would increase at a constant rate, as presented in the conceptual representation in Figure 17. In the presence of tillage management, it is here assumed that, when tillage takes place, SOM drops to its original levels. Although after a tillage event, a decrease in SOM level is expected, the final level is not known. We assume the same slope for the two sub-‐sets of data.
52
F IGURE 16 – SOM LEVELS VERSUS YSLM FOR THE ALL DATA
SOM (gSOM/100gsoil)
Tillage No-tillage
time
F IGURE 17 – S CHEMATIC REPRESENTATION OF SOM EVOLUTION ACCORDING TO THE MANAGEMENT SYSTEM
3 .2 .3 C A R B O N S E Q U E S T R A T I O N F A C T O R The estimation of the carbon sequestration factor (tCO2.ha-‐1.yr-‐1) is based on an estimated annual change in SOM levels ((gSOM/100gsoil).yr-‐1). When considering the results from the t-‐test, we assumed that the difference in SOM levels correspond to the change that would take place with the shift from tillage to no-‐tillage management. As stated before, the SOM levels can be converted into soil carbon by a factor of 0.58 (gSOC/gSOM). The carbon content per area is estimated using the bulk density (gsoil.cm-‐3) and sampling depth (cm). As already discussed, the sampling depth is 20 cm. The bulk density was not assessed on field, and as such we recurred to secondary sources. According to Teixeira et al. (2011), the average soil bulk density in Portuguese soils is 1.48 gsoilcm-‐3. Using the same source of information, the HWSDB (Fisher et al., 2008), for the sampled plots, the bulk density was gathered and its average used in the analysis. The obtained values are of 1.35 and 1.52 gsoilcm-‐3, corresponding to two different approaches used by Fisher et al. (2008) to estimate bulk density. The conversion of carbon to carbon dioxide is done with the factor 44 12.
53
In order to adjust the obtained value for the presence of rocks and roots, we include a factor of 15%, following the data from LUCAS database and grassland occupation6.
3.3.
R E S U L T S & D IS C U S S IO N
3 .3 .1 S T A T I S T I C A L A N A L Y S I S O F S O M D I F F E R E N C E S 3 . 3 . 1 . 1 T W O S A M P L E M E A N H Y P O T H E S I S A P P R O A C H
The results regarding SOM concentration averages and standard deviations are presented in Table 10. The quartile distributions are presented in Figure 18 and Figure 19. T ABLE 10 – SOM AVERAGE AND STANDARD DEVIATION Soil original material All Granite Sandstone Schist
SOM (gSOM/100gsoil) 2011 Standard deviation Average 1.86 2.87 0.91 1.82 2.25 3.10 0.74 1.88 0.49 1.50 0.63 1.61 1.48 3.94 1.31 2.48
Management Average 3.08 2.01 3.85 2.32 1.44 1.57 4.26 2.85
No-‐till Till No-‐till Till No-‐till Till No-‐till Till
2012 Standard deviation 1.62 0.74 1.78 0.48 0.43 0.71 1.39 1.05
0
2
SOM (gSOM/100gsoil) 4 6
8
NT
T
2011
NT
T
2012
F IGURE 18 – B OX PLOT FOR SOM DATA BY SYSTEM AND SAMPLING YEAR
6
For more information please check http://eusoils.jrc.ec.europa.eu/projects/Lucas/
54
8 SOM (gSOM/100gsoil) 2 4 6 0
2011
2012
Granite
2011
2012
2011
Sandstones
2012
Schist
NT
T
F IGURE 19 – B OX PLOT FOR SOM DATA BY SYSTEM AND SAMPLING YEAR AND ORIGINAL MATERIAL
Using the entire dataset two results are highlighted: (1) average SOM concentration is higher for data regarding no-‐tilled plots, and (2) 2011 values are higher than 2012 ones. Also, the SOM dispersion is higher for data regarding no-‐tilled plots. The time frame between 2011 and 2012 was characterised by a drought, which may explain the lower values. For a 95% CI, SOM data fails the Bartlett’s test for homoscedasticity as well as the Shapiro-‐Wilk test for normality. As presented in Figure 20, Figure 21 and Figure 22 the graphical assessment for normality shows that SOM data roughly follows a normal distribution, although it is possible to
0
0
2
2
SOM (gSOM/100gsoil) 4 6
SOM (gSOM/100gsoil) 4 6
8
8
identify both problems of skewness and kurtosis. No-‐tillage data presents a worse performance.
NT
T Frequency
NT
T Frequency
F IGURE 20 – D ISTRIBUTIONAL DOTPLOT FOR 2011 ( LEFT ) AND 2012 ( RIGHT ) SOM DATA
55
8
8
SOM (gSOM/100gsoil) 4 6
6
2
SOM (gSOM/100gsoil) 2 4 0
0
-2 -2
0
2 4 Inverse Normal
6
0
8
1
2 Inverse Normal
3
4
F IGURE 21 – C OMPARISON WITH NORMAL QUARTILE FOR NO -‐ TILLAGE ( LEFT ) AND TILLAGE ( RIGHT ) SOM DATA , 2011
8 0
0
2
SOM (gSOM/100gsoil) 4 6
SOM (gSOM/100gsoil) 2 4 6
8
0
2
4
6
Inverse Normal
0
1
2 Inverse Normal
3
4
F IGURE 22 – C OMPARISON WITH NORMAL QUARTILE FOR NO -‐ TILLAGE ( LEFT ) AND TILLAGE ( RIGHT ) SOM DATA , 2012
In order to transform SOM variable into a new variable with normal distribution, a logarithmic
-1
-1
0
0
ln(SOM)
ln(SOM)
1
1
2
2
transformation is applied (base e). Although other transformations were analysed, such as the Box-‐ Cox, only the logarithmic (base e) is here presented. The Shapiro-‐Wilk’s test, for a 95% CI, does not allow the rejection of normality (although it would be rejected with a 99% CI). In Figure 23, Figure 24 and Figure 25 we present graphical assessment for normality. As for homoscedasticity, the t-‐test was defined to accommodate different variances.
NT
NT
T Frequency
T Frequency
F IGURE 23 – D ISTRIBUTIONAL DOTPLOT FOR 2011 ( LEFT ) AND 2012 ( RIGHT ) LN (SOM) DATA
56
2
3
1
2
0
ln(SOM)
ln(SOM) 1
-1
0 -1 -1
0
1 Inverse Normal
2
3
-.5
0
.5 Inverse Normal
1
1.5
F IGURE 24 – C OMPARISON WITH NORMAL QUARTILE FOR NO -‐ TILLAGE ( LEFT ) AND TILLAGE ( RIGHT ) LN (SOM), 2011
ln(SOM) -1
-1
0
0
ln(SOM)
1
1
2
2
-.5
0
.5
1 Inverse Normal
1.5
2
-.5
0
.5 Inverse Normal
1
1.5
F IGURE 25 – C OMPARISON WITH NORMAL QUARTILE FOR NO -‐ TILLAGE ( LEFT ) AND TILLAGE ( RIGHT ) LN (SOM), 2012
The results from the t-‐test, applied to the transformed data are presented in Table 11, Figure 26 and Figure 27. According to these results (and considering the entire dataset), there is no overlap in the 95% CI. The null hypothesis of equality of means is rejected with a p-‐value of 0.00 for both 2011 and 2012 data. The robustness of this result is supported by the non-‐parametric test of equal means hypothesis for SOM data. With a p-‐value of 0.00 both for 2011 and 2012 data, the null hypothesis is rejected by the Kruskal-‐Wallis test. Summing up, the statistical analysis indicates that, for the analysed dataset and a 95% CI, the average SOM concentration is not equal among treatments, with higher values for data regarding plots under no tillage. T ABLE 11 – T WO SAMPLE T -‐ TEST RESULTS FOR LN (SOM) DATA (95% CI) Soil original material All Granite Sandstone Schist
Management No-‐till Till No-‐till Till No-‐till Till No-‐till Till
Low 2.16 1.62 1.82 1.97 1.18 1.24 3.50 1.72
SOM (gSOM/100gsoil) 2011 2012 Average High Low Average 2.54 2.98 2.14 2.46 1.81 2.02 1.54 1.68 3.35 6.17 1.53 2.79 2.22 2.50 1.65 1.82 1.36 1.57 1.29 1.45 1.44 1.66 1.27 1.47 4.00 4.58 3.30 3.72 2.51 3.66 1.68 2.28
High 2.84 1.84 5.10 2.00 1.62 1.69 4.20 3.07
57
All$
Granite$
Sandstones$
Schist$
6.5$ 6.0$ 5.5$
SOM$(gSOM/100gsoil)$
5.0$ 4.5$ 4.0$ 3.5$ 3.0$ 2.5$ 2.0$ 1.5$ 1.0$
No*+llage' Tillage'
F IGURE 26 – S CHEMATIC REPRESENTATION OF T -‐ TEST RESULT FOR LN (SOM) DATA (95% CI), 2011
All$
Granite$
Sandstones$
Schist$
5.5$ 5.0$
SOM$(gSOM/100gsoil)$
4.5$ 4.0$ 3.5$ 3.0$ 2.5$ 2.0$ 1.5$ 1.0$
No*+llage' Tillage'
F IGURE 27 – S CHEMATIC REPRESENTATION OF T -‐ TEST RESULT FOR LN (SOM) DATA (95% CI), 2012
Since the t-‐test method is able to accommodate a certain degree of violation of its assumptions (Zar, 2010), this method was also applied to SOM data (without transformation). According to the obtained results (see Figure 28 and Figure 29), for 2011 data, and a 95% CI, SOM concentration was 2.01±0.20 for tilled plots and 3.08±0.46 for no-‐tilled plots; for 2012 data, SOM concentration was 1.82±0.16 for tilled plots and 2.87±0.41 for no-‐tilled plots. The equality of means is also clearly rejected, with a higher SOM concentration for the data correspondent to no-‐tilled plots.
58
All$
Granite$
Sandstones$
Schist$
6.5$ 6.0$ 5.5$
SOM$(gSOM/100gsoil)$
5.0$ 4.5$ 4.0$ 3.5$ 3.0$ 2.5$ 2.0$ 1.5$ 1.0$
No*+llage' Tillage'
F IGURE 28 – S CHEMATIC REPRESENTATION OF T -‐ TEST RESULT FOR SOM DATA (95% CI), 2011
All#
Granite#
Sandstones#
Schist#
6.0# 5.5# 5.0#
SOM$(gSOM/100gsoil)$
4.5# 4.0# 3.5# 3.0# 2.5# 2.0# 1.5# 1.0# 0.5# 0.0#
No*+llage' Tillage'
F IGURE 29 – S CHEMATIC REPRESENTATION OF T -‐ TEST RESULT FOR SOM DATA (95% CI), 2012
When the distinction in soil original material is included, each sample becomes necessarily smaller and more unbalanced, with a loss in statistical testing strength. The results from Table 10 can be separated in two groups. The first group regards the sandstones, for which SOM concentration is higher for data regarding tilled plots. The second group includes granite and schist, where SOM concentration is higher for data regarding no-‐tilled plots. The Shapiro-‐Wilk test for normality indicates mixed results. While for 2011 data the hypothesis of normality is not rejected for the sub-‐sample respecting to any of the soil original material, for 2012 data, normality is rejected for all soil original materials. Similarly mixed results are obtained in the Bartlett’s test for homoscedasticity. Again, we tested a logarithmic transformation of each sub-‐set. T-‐test results are presented in Table 11 and show an important overlap in SOM concentrations for the sandstones and granite. The p-‐values are 0.58 and 0.15, respectively and regarding the 2011 data; and 0.88 and
59
0.12 for the 2012 data. The hypothesis of equal means can not be rejected. For the schist sub-‐set of data, there is a slight overlap for 2011 data, but not for 2012 data. The correspondent p-‐values are 0.02 and 0.00 for 2011 and 2012, respectively. Therefore, for a 95% CI, it is possible to reject the hypothesis of equal means. The same results are obtained for the t-‐test performed to the non-‐transformed data. For the plots originated from granite, tillage samples present a 95% CI of 2.32±0.28 for 2011 data and of 1.88±0.18 for 2012. No-‐tillage samples present a 95% CI of 3.85±2.36 for 2011 and 3.10±2.21 for 2012. For the plots originated from sandstone, the till samples present a 95% CI of 1.57±0.20 for 2011 data and of 1.61±0.22 for 2012. The no-‐till plots range from 1.44±0.20 for 2011 and 1.50±0.17 for 2012. For the plots originated from schist, the till samples present a 95% CI of 2.85±0.88 for 2011 data and of 2.48±0.70 for 2012. The no-‐till plots range from 4.26±0.54 for 2011 and 3.94±0.50 for 2012. Regarding 2011 data, previous results are coherent with those from the non-‐parametric Kruskal-‐ Wallis test, which reject the null hypothesis of equal means for schist data and does not reject it for granite and sandstone. As for 2012 results, results for granite are not coherent for the 95% CI, but coherent for a 99% CI. Summing up, when soil original material is not included, the null hypothesis of equal SOM average among management techniques is rejected with a 95% CI, indicating higher SOM concentration for data regarding no-‐tilled plots. When soil original material is included mixed results are obtained and it is not possible to draw a solid conclusion. The lower, average and higher SOM concentration differences, in a yearly basis, are presented in Table 12. T ABLE 12 – H IGHER AND LOWER ΔSOM FROM THE T -‐ TEST RESULTS (95% CI) -‐1
Soil original material All
ΔSOM ((gSOM/100gsoil).yr )
Data SOM ln(SOM)
Lower 0.06 0.02
2011 Average 0.17 0.11
Higher 0.31 0.25
Lower 0.06 0.04
2012 Average 0.14 0.11
Higher 0.25 0.20
3 . 3 . 1 . 2 M U L T I P L E L I N E A R R E G R E S S I O N A P P R O A C H
As for homogeneity of variables, in Table 13 we present those for which the null hypothesis of equal distribution between control systems is rejected (p-‐value
