Resource conservation, tools for screening climate smart practices and public participation

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Natural resources continue to play an important role in livelihood and wellbeing of millions. Over exploitation and degradation of natural resource base have led to declining factor productivity in rural areas and dwindling farm profits coupled with debilitating impact on human health. This necessitates promoting technologies that can help producing food keeping pace with the growing population while conserving natural resource base and be profitable. Achieving this conflicting target though appears to be challenging but is possible with the currently available technologies. This lecture will provide insights into a gamut of resource conserving technologies, the role of communities in promoting them and tools that can help in identifying suitable technologies for adoption. The lecture will heavily borrow sustainable agriculture cases from the Asia Pacific region.

Outline
•    Natural resource dependency and rural development
o Trends in resource depletion and impact on food production
o Farm profitability trends and input use
o Trends in factor productivity
•    Resource conserving technologies and climate smart agriculture
o What are they?
o Similarities and differences
o Costs and benefits of pursuing them
•    Tools for identifying resource conserving and climate smart agriculture technologies
o Factor productivity
o Benefit cost ratios
o Marginal abatement costs
•    Role of communities
o Communities as entry point
o Benefits of community participation
•    Concluding thoughts
o How to scale up resource conservation?

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  • http://www.teebweb.org/national-and-international-policy-making-figures-and-graphs/
  • http://www.teebweb.org/national-and-international-policy-making-figures-and-graphs/
  • http://www.teebweb.org/national-and-international-policy-making-figures-and-graphs/
  • http://www.teebweb.org/national-and-international-policy-making-figures-and-graphs/http://www.fao.org/docrep/015/i1688e/i1688e00.pdf
  • http://www.sciencedirect.com/science/article/pii/S1574007209040729
  • http://www.fao.org/climatechange/54270/en/http://www.fao.org/geonetwork/srv/en/graphover.show?id=14070&fname=Map_5_05.png&access=public
  • http://www.sciencedirect.com/science/article/pii/S1462901104000978http://www.sciencedirect.com/science/article/pii/S0378377405003197
  • http://www.sciencedirect.com/science/article/pii/S0378377409001711
  • Exposure stands for exposure to physical events. Developing countries in AP region are inherently highly exposed to physical climatic events such as droughts floods and typhoons due to their geographical location and climatic conditions. Sensitivity refers to the degree to which the system is affected by a physical event, the higher the sensitivity the more the impactsAdaptive capacity refers to the ability to make adjustments to reduce the impacts. This would modify the potential impacts into what is seen the real impacts. In absence of adaptive capacity, the observed impacts would equal to the potential impacts.
  • Exposure refers to exposure of particular system to stimuli such as temperature rise, natural hazards, sea level rise etc. It can be seen in this diagram that Asia is exposed to more number of natural disasters than other regions in the world. This is mainly due to the geographical and climatic conditions inherent to the region even without considering the capacity and vulnerability aspects of the society living in the region. The image shows the data during 1991-2000Source: http://www.unisdr.org/disaster-statistics/other/natural-disasters-by-origin.htmhttp://www.unisdr.org/disaster-statistics/occurrence-type-disas.htm
  • Sensitivity refers to the responsiveness of the system to the climate change stimuli such as natural hazards. Higher the sensitivity of a system higher the impacts it will face due to the changes in the system.
  • As an example in AP region, these two figures vindicate the global trend as applicable for the AP region as well. Among all the hydro-met disasters in AP region, the frequency and intensity of floods have increased at an alarming rate in the past 3-4 decades.
  • South Asia countries: Afghanistan  India  Pakistan  Bangladesh  Maldives  Sri Lanka  Bhutan  Nepal East Asia Pacific countries: American Samoa  Malaysia  Samoa  Cambodia  Marshall Islands  Solomon Islands  China  Micronesia, Fed. Sts  Thailand  Fiji  Mongolia  Timor-Leste  Indonesia  Myanmar  Tonga  Kiribati  Palau  Vanuatu  Korea, Dem. Rep.  Papua New Guinea  Vietnam  Lao PDR  Philippines   The World Bank’s IDA Resource Allocation Index (IRAI) is based on the results of the annual CPIA exercise that covers the IDA eligible countries. The CPIA rates countries against a set of 16 criteria grouped in four clusters: (a) economic management; (b) structural policies; (c) policies for social inclusion and equity; and (d) public sector management and institutions. The  criteria are focused on balancing the capture of the key factors that foster growth and poverty reduction, with the need to avoid undue burden on the assessment process. To fully underscore the importance of the CPIA in the IDA Performance Based Allocations, the overall country score is referred to as the IRAI.The International Development Asso- ciation (IDA) is the part of the World Bank that helps the world’s poorest countries. Established in 1960, IDA aims to reduce poverty by providing interest-free credits and grants for programs that boost economic growth, reduce inequalities and improve people’s living conditions.IDA complements the World Bank’s other lending arm–the International Bank for Reconstruction and Development (IBRD)–which serves middle-income countries with capital investment and advisory services. IBRD and IDA share the same staff and headquarters and evaluate projects with the same rigorous standards.IDA is one of the largest sources of assistance for the world’s 79 poorest countries, 39 of which are in Africa. It is the single largest source of donor funds for basic social services in the poorest countries.IDA lends money (known as credits) on concessional terms. This means that IDA credits have no interest charge and repayments are stretched over 35 to 40 years, including a 10-year grace period. IDA also provides grants to countries at risk of debt distress.Since its inception, IDA credits and grants have totaled US$207 billion, averaging US$12 billion a year in recent years and directing the largest share, about 50 percent, to Africa.
  • http://www.fao.org/tc/exact/ex-act-home/ex-act-and-climate-smart-agriculture/en/http://www.fao.org/climatechange/climatesmart/en/
  • http://brr.berkeley.edu/rci/
  • http://go.worldbank.org/1U8MK1UZC1
  • http://journals.cambridge.org/action/displayAbstract;jsessionid=595A34767E255F77DBAC880F4F6D7ACB.journals?fromPage=online&aid=7884406
  • Resource conservation, tools for screening climate smart practices and public participation

    1. 1. Resource Conservation, Tools forScreening Climate SmartPractices and Role ofCommunitiesSVRK Prabhakar, IGES, Japanprabhakar@iges.or.jpPresented at World Bank Blended Learning Program on Policies and Practices for NaturalResource Management, 14 March – 31 May 2013, World Bank TDLC, Tokyo, Japan
    2. 2. • Trends in natural resource use and globalchange impacts• Resource conservation technologies forclimate smart agriculture• Tools for identifying appropriate technologies• Role of communities in resource conservation• Concluding thoughts2
    3. 3. Friends of the Earth, 20093
    4. 4. Notes:Forest cover: areas with a canopy cover of at least 40% by woody plants taller than 5 meters4
    5. 5. 5
    6. 6. 6
    7. 7. Source: FAO, 20117
    8. 8. 8LACAsia AfricaSource: Avila and Evenson, 2010
    9. 9. 9
    10. 10. 10Source: Aggarwal et al., 2004Source: Ambast et al., 2006Trend in Trans-Gangetic areaOverall Trend
    11. 11. 11Mainuddin and Kirby, 2009
    12. 12. • GHG emissions are rapidly growing for all countries with higher rate indeveloping countries after 1975• However, per capita emissions of developing countries are 1/4th of thedeveloped countries (historically it is 1/13th)12GHG EmissionsData source: WRI CAIT, 2009
    13. 13. 13World Bank, 2009• Land-use changes contribute tosecond largest emissions afterpower in middle incomecountries• In low-income countries, LUCscan account up to 50% of totalemissions followed byagriculture
    14. 14. • In terms of absolute quantity, 20.5% GHG emissions from non-Annex Icountries is equivalent to 3748.5 MtCO2e which is double the GHGemissions from Agriculture sector from Annex-1 countries.• Globally, agriculture accounts to 47% and 58% of global anthropogenicmethane and nitrous oxide emissions.(73.3)(7.1)(16.3)(3.4)(85.1)(4.0)(8.3)(2.6)14
    15. 15. • Significant land use emissions are CO2, CH4and N2O.• CO2 emissions are not considered since thecrop is expected to sequester the emissions inthe next season.• Most of the CH4 and N2O emissions can beattributed to paddy cultivation, residueburning, composting, use of manures andnutrients, irrigation water management.15
    16. 16. • That fraction of landuse changes attributedto pressure fromagricultural demand forland (agriculture asdriver), mostly CO2.• Mostly estimated fromactual land expansionunder agriculture.FAOSTAT, 200916
    17. 17. 17
    18. 18. • Continuous increase in farmmechanization with decline in farm animaldraft power.• Decline in organic matter input and morereliance on inorganic fertilizers.• Over exploitation of groundwater forirrigation which needs substantialpumping.• Increasing burning of paddy straw andother farm residues due to increasedcropping intensity.18
    19. 19. • Non-CO2 emissions will continue to increasein agriculture sector (US-EPA 2006, IPCC2007, Stern 2007)• Most increases are to come from– Methane: rice paddies, entericfermentation, manure, and burning straw– N2O: fertilizations, manure, soils, straw burning19
    20. 20. World Bank, 2009• There is high potential for low cost mitigation options in developing countries• Agriculture and forestry form some of the low-cost mitigation options20
    21. 21. 21Source:ADB,2009Estimated costs of damage from floods and storms
    22. 22. 22Global warmingwould negativelyimpactagricultural yieldsin most of thedeveloping worldNegative impactsare higher inabsence ofcarbon dioxidefertilizationCline, 2007
    23. 23. • Greater food security challenges for South Asia due todecline in rice and wheat yields and area under wheat• Decline in freshwater availability in many parts of Asia• Spring flooding and irrigation shortage in South Asia• Coastal flooding due to SLR in South, East and South-East Asia with –ve impact on Asian Megadeltas• Enhanced glacial melt and related outbursts inHimalayan region• Change in natural vegetation types• Increase in malaria and cholera in South and CentralAsia23
    24. 24. 24Net high impacts in Asia Pacific region due to high exposure, high sensitivity and poor adaptive capacity• Climate change impacts are function of exposure, sensitivity, and adaptive capacity.• Impacts are directly proportional to exposure and sensitivity and indirectly proportional toadaptive capacity.
    25. 25. • High incidence of hydro-met events such as droughts, floods, cyclones/typhoons, heat waves etcin the highly populated Asia.25Source: ISDR, 2009
    26. 26. • High poverty levels, especially in rural areas (500 millionsubsistence farmers in AP region), characterized by low humandevelopment index• High dependency on primary production sectors such asagriculture and animal husbandry (nearly 60% of totalpopulation), that are directly impacted by climate change, coupledwith lack of diversified livelihood options• Least access to resources (inequality) coupled with rapiddegradation of natural resource base including forests• Poor governance and institutional systems(political, social, environmental and economic) reflectingfragmented and slow progress in development26
    27. 27. 27India VietnamSource: EMDAT, 2007Source: EMDAT, 2007CountryGDP per capita(USD)Population (million)Number oftyphoonsFatalities Fatalities per eventJapan 38,160 126 13 352 27Philippines 1,200 74 39 6,835 175Bangladesh 360 124 14 151,045 10,788Source: Mechler, 2004
    28. 28. Determinants of adaptive capacity Developing SouthAsiaDeveloping East AsiaPacificWorldPer capita GNI, PPP basis (USD) 2733 5399 10,357Technology patent applications(total since 2000)129,035 1,214,326 12,420,319% of paved roads in total (proxy) 30.8 (2000)[57 (2004)]11.4 (2000) 36 (2000)Resource allocation (IRAI, rated on1-6 scale)3.5(IDA countries)3.3(IDA countries)3.3(IDA countries)The World Bank, 2009; WIPO, 2009• Developing South Asia lag in economic development and technology exports• Developing East Asia Pacific lag in infrastructure and resource allocation28
    29. 29. 29Source:ADB,2009• Adaptation benefits aremuch higher than thecosts in the 4 countries ofSouth East Asia(Indonesia, Philippines, Thailand, and Vietnam;Figure on left)• By 2100, the benefits ofadaptation would reach tothe tune of 1.9% of GDPwhen compared to costsat 0.2%
    30. 30. • “An agriculture that sustainably increasesproductivity, resilience (adaptation), reduces/removes GHGs(mitigation), and enhances the achievement of national foodsecurity and development goals” (FAO, 2010)31Achieved through(FAO, 2013)
    31. 31. – Zero-tillage (or) conservation tillage(wheat)– Windrow Composting (Paddy straw)– Leaf color charts (Rice)– System Rice Intensification (Rice)– Alternative nutrient sources andamendments (Rice)– Carbon sequestration– Use of RE in agriculture– Mid-season drainage– Alternate flooding and drying32
    32. 32. Rice stubbles retained/ mulched• Zero-Tillage saves 70-90 L ofdiesel/ha• Saves water (to the tune of ~1.0x106L water)• Farmers save USD 40-55/ha.• Reduced/ eliminate burning of cropresidues33Climate and economic benefitsSource: RWC, 2005
    33. 33. • US Environmental ProtectionAgency and US CompostingCouncil:– Aerobic composting doestcontribute to CO2 emissions– It is considered as natural cycle– Eliminates CH4 and N2Oemissions34Collection of Paddy StrawChopping to short pieces forquick compostingMixing Straw with inoculumFormation of layered windrowsof 2-4 m long and 1-2 m heightMixing once in 20-30 daysSieving and segregation
    34. 34. Treatment N appliedkg ha-1Gr. Yield, kgha-1PFP-N N saved,kg ha-1FP 149 6359 42.7 -LCC-N 124 6371 51.4 25Source: RWC, 200535
    35. 35. 36• Refers to a combination of technologies forsaving irrigation water, fertilizer inputs andincrease farm profitability.• Involves transplanting of young seedlings inone seedling per hill in rows, intermittentirrigation and drainage practice.• Substantial gains inyields, reduced lossesfrom leaching, reducedmethane emissions.
    36. 36. GHGMMcMAC38MAC = Marginal abatement cost ($t-1)Mct = Marginal cost of the new technology when compared to the baselinetechnologyMGHG= Marginal reductions in GHG emissionsba CCMcbaGHG GHGGHGMCa= Cost of technology aCb = Cost of technology b
    37. 37. Activity data: E.g. area under particular technology or amount of biomassburnt or amount of particular fertilizer type usedEf: Emission factor, factor that provides GHG quantity by multiplication withthe activity dataSf: Scaling factor, factor that modifies a sub-practice from the base linepractice (e.g. intermittent irrigation as against continuous flooding)Notes:– Data sources: 2006 IPCC guidelines for national GHGinventories, Secondary sources such as journal papers– Logic: Urea not used is not produced! (different from IPCC approach)– Currently the calculations are between Tier I and Tier II approachessuggested by IPCC. Need to be standardized to either one of the Tiers.39SfEfActivityGHGa
    38. 38. 40CostsOperational costsHuman laborBullock laborMachine laborSeedFertilizers and manuresFertilizersManureInsecticideIrrigationInterest on working capitalFixed costRental value of owned landLand taxDepreciation on implements and farm buildingsInterest on fixed capitalGross Income (GI)Yield per ha (t/ha)Value of main product per haValue of by product per haCost : Benefit RatioTotalCostsitsTotalBenefBCR• Notes: Positive and negative externalities can also be considered
    39. 39. 41Zero-till LCCSystem Rice IntensificationComposting
    40. 40. • India’s agricultural GHG emissions in 2005 were 402.7 Mt CO2-e per year.• Compare 116 Mt CO2-e mitigation potential of the above 4 technologiesalone.
    41. 41. • Provides ex-ante estimations of net carbonbalance of GHG estimations and sequestrationin agriculture and forestry developmentprojects• Aimed at– increasing the accuracy of carbon accounting– Supports investments in climate smart agriculture– useful in policy analysis• Land-based accounting system that comparesland management options with BAU scenario
    42. 42. • Resilience/adaptation componentsare still being developed• No cost-benefit analysis forcomparing options• Requires a good training forgetting full potential out of thetool• Appears to be daunting and onlysuitable for medium to large sizedprojects (con) but one can soonfamiliarize with it
    43. 43. • Resilience Capacity Index (RCI): is a singlestatistic summarizing a region’s score on 12equally weighted indicators—four indicators ineach of three dimensions encompassingRegional Economic, Socio-Demographic, andCommunity Connectivity attributes(UCB, 2013)– Generic framework that can be ported to anydevelopment situation
    44. 44. • Index of Usefulness of Practices forAdaptation to climate change (IUPA) Index(Claudio Szlafsztein, Federal University ofPara, Brazil)– Integrates both qualitative and quantitativeparameters into a single index– Choosing the weightings for individualparameters is a question
    45. 45. LaIn=40*)(/*)()(60*)(/*)()(.ln.ln.Re.ReVuallIndexVui iallialliadallIndexadi iallialliScoreMaxWeightIndexStdevIndexMeanIndexScoreMaxWeightIndexStdevIndexMeanIndexSource: based on GAIN, 2011
    46. 46. 01 AcAcAexWhere:Aex: Effectiveness of adaptation action x;Ac0, Ac1: LaIn values at times T1 and T2Ix, Iy, Iz: adaptation actions 49
    47. 47. Review Literature for identifying indicators, RegionalConsultation (Year I)Indicator vetting through Participatory Appraisal Processes (Yr. II-III)Integrating LaIn into local decision making mechanisms (Yr. III-V)Focused group discussions and ranking ofindicators and criteria with researchers, localadministration, and NGOs etc in each projectcountry in GMS region (Yr. II)Developing draft questionnaires for inputs fromcommunities, local administration, NGOs andresearchers (Yr. II)Conduct pilot questionnaire surveys to test theusability of questionnaires (Yr. II)Conduct actual surveys for identifying localeffectiveness indicators (Yr. III)Participatory ranking ofindicators and criteriaQuantification of indicatorsIncorporation of local effectiveness indicatorsinto GaIn computation for arriving at LaIn (Yr. III)Conduct consultations with local admin andNGOs etc to identify strengths and weaknessesfor mainstreaming LaIn into their decisionmaking processAdaptation Metrics Adaptation DecisionMaking Framework
    48. 48. Indicators (Bangladesh, based on pilot survey)Vulnerability •% farms with soil degradation (exposure)•% soil cover (exposure)•Period of fresh water availability (exposure)•Area under high water use crops (sensitivity)•Area under arable farming (sensitivity)•Soil organic matter content (capacity)•Area under reduced tillage (capacity)Readiness •% of households having access to credit (economic)•% of households having access to markets (economic)
    49. 49. Indicators (Bangladesh, basedon pilot survey)Value Range(Min Max)Score WeightVuln. •% Soil degradation 20 5-30 0.67 0.14•% soil cover 40 10-70 0.57 0.14•Period of water availability(days)120 50-200 0.60 0.14•Water int. crops (ha) 50 40-60 0.83 0.14•Arable farming (ha) 80 40-90 0.89 0.14•Soil OM content (%) 0.5 0.25-1 0.50 0.14•Reduced tillage (ha) 10 5-60 0.17 0.14Read. •Households credit access (%) 40 10-80 0.50 0.50•Farmers access to markets (%) 50 20-80 0.63 0.50Indicator values for BAU practiceNote: Scores are calculated by linear normalization with thresholds
    50. 50. Indicators (Bangladesh, basedon pilot survey)Value Range(Min Max)Score WeightVuln. •% Soil degradation 5 5-30 0.17 0.14•% soil cover 70 10-70 1.00 0.14•Period of water availability (days) 180 50-200 0.90 0.14•Water int. crops (ha) 30 40-60 0.50 0.14•Arable farming (ha) 80 40-90 0.89 0.14•Soil OM content (%) 0.75 0.25-1 0.75 0.14•Reduced tillage (ha) 40 5-60 0.67 0.14Read. •Households credit access (%) 50 10-80 0.63 0.50•Farmers access to markets (%) 60 20-80 0.75 0.50Indicator values for ZT practiceNote: Scores are calculated by linear normalization with thresholds
    51. 51. 1. Proximity to and dependency on resources: Communities live closeto natural resources, they are benefited by them and hence can beeffective stewards of those resources2. Equity: Communities have diverse interests in natural resources andachieving a consensus on benefit sharing is an important aspect3. Capacity: Communities often have better understanding on resourcesthat they live in proximity than other stakeholders4. Biodiversity. Multi-purpose management of natural resources bycommunities often have higher biodiversity benefits than singlepurpose management by other stakeholders5. Cost-effectiveness: Local management may help reduce governmentcosts.6. Development philosophy. Local participation, decentralization, andsubsidiarity may, in themselves, be considered importantdevelopment objectives. Source: World Bank, 2011
    52. 52. • Conventional NRM that is based on optimizingand maximizing sustainable yield of singleresource has met challenges from various globalchanges being faced• ESS considers sustaining the capacity ofecosystems to provide services that benefit thesociety by linking the integrity and diversity ofecosystems with the adaptive capacity andsocietal wellbeing (Chapin et al., 2009)
    53. 53. • Development of technologies and livelihood options byinvolving communities right from the beginning insteadof seeing communities as ‘end of the pipebeneficiaries’. Some examples:– Chipko movement of forest conservation in Garhwal regionof Uttarakhand, India– Participatory R&D of resource conservation technologies inthe Gangetic basin by the Rice-Wheat Consortium– Numerous examples in watershed management, soilconservation, fishery management, payment of ecosystemservices, agroforestry, catchment protection, livelihooddiversification etc.
    54. 54. • ZT area in entire South Asia: 2 M ha.• Adoption of other crop technologies is in subthousand hectares.• Annually, an estimated 35 million tons ofpaddy straw is being burnt in India, Thailandand Philippines even today.
    55. 55. • No incentives for adopting GHG mitigationtechnologies.• The technologies with high abatementpotential doesn’t necessary to have highbenefits per unit investment which farmersconsider more (e.g. SRI, LCC as against ZT).
    56. 56. • Solving the puzzle of agricultural input subsidies.• Incentives [and disincentives] for agriculturalpractices with high [low] conservation benefits.• Market mechanisms (Carbon sequestration in soiland price on carbon)?• Enhanced technology transfer from labs to fields.
    57. 57. • Various agricultural drivers leading toland use changes– Poor productivity– Degrading natural resource base (decliningfactor productivity: e.g. as in case of Indo-Gangetic Plains)– Absence of alternative livelihoods duringstress periods (droughts and floods)
    58. 58. • Increase in productivity of above crops in China, India, Indonesia, Malaysia, Thailand and Vietnamcan free a maximum of ~90 Mha of land• A 0.5% increase in productivity of above key crops can free more land lost to deforestation in thelast 15 years in Asia (Asia lost 2.9 Mha of forests during 1990-2005)
    59. 59. Thank You!prabhakar@iges.or.jp

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