Farm- scale greenhouse gas emissions using the Cool Farm Tool: application of a generic farming emissions calculator in developing countries

Emissions are a function of diverse factors such as management, climate, geography, and technology, so it may not be fruitful to search for "onesize-fits-all" mitigation policies, since they will not be optimal for the majority of farms or cropping systems (Smith et al. 2008). As such, there has been interest in the development of methods and software tools which estimate emissions at the farm scale (where management decisions are often made). Table 19.1 provides a summary of the types of methods available for agricultural greenhouse gas (GHG) emissions quantification. Large uncertainties surround emissions quantification, particularly in respect to the dependence of emissions on factors that vary over small spatial scales. By nature, agricultural systems are heterogeneous, even at small scales, which means that for certain components of a product life cycle there is always significant uncertainty. As an example, the nitrification and denitrification reactions leading to nitrous oxide (N2O) emissions, although known to be functions of soil structure, soil nitrate concentration, and soil water content, are difficult to characterise due to physical variation even at small temporal and spatial scales (i.e., soil heterogeneity and weather). As such, prediction of N2O emissions can, in theory, always be improved with additional information, although the quality of prediction is difficult to quantify since even measurement techniques areTa ble 19.1 Sum mar yof agr icul ture em issi ons quan tifi cati onm eth odsC ompl exity Mod els Dat are quir emen tsA ggre gatio nle vel/ unce rtai nty Not esT ier 1IP CC Tie r1 defa ultf acto rsL imit edla nd use and man agem ent acti vity dat a(e .g., Na pplic atio nr ates ;ac res unde rn o-ti ll);l ittl eso ilde linea tion ;an imal pop ulat ion sL owd ata inpu tsT ypic ally larg esp atia lun its;n atio nal sca le; ann ualr esol utio n; hig hes tun cert ain tyw hen app lied atpr ojec tsca leSu itab lefo rro ugh ove rvie ws and wh ere limit edd ata isavai labl e(e .g., indi rect Nem issi onfa ctor fromle ach ing)T ier 2H ybri dap proa ches –us ing proc ess ore mpi rica lm odel sto deve lop regi on-s peci fic empi rica leq uati ons wit he mis sionfa ctor sIn term edia tes pati al/t empo ral scal ein putd ata; lan d-us ean dac tivi tyd ata scal edto the spat ial unit ofa nal ysis (ti llage type s,an imal cla sses ,Nfe rtili zer type ;cr opty pe); Req uire slo nge r-te rms cien tifi cda tato dev elop em piri calm odel sor cal ibra tep roce ssm odel sFi ner spa tial an dte mpo ral reso luti onth anab ove; can ach ieve reas onab leun cert ain tyd ueto 'ave ragi ng' of mod elle dre sult sC anb esu itab lefo rpr ojec t-bas edac coun tin gan din ven tory rol l-ups ton atio nal sca le; appl icat ion will depe nd ona vaila ble scie nti fic and man agem entd ataT ier 3Pr oces s-ba sed mod els Spat ially exp licit fin e-sc ale data for mod elv aria bles ;det aile dla nduse and man agem enth isto ries ;fi nescal eso ilm aps and daily /wee kly clim ate data ;re quir ese xten sive sci enti fic info rmat ion toc alib rate mod els atth iss cale ;fi eld mea sure dda tafo res tim atin gun cert ain tyis oft enlim itin gfa ctorFi nes tspa tialsc ale wit hre pres enta tion of envi ron men tala nd man agem ent vari able sat the indi vidu alfa rmle velSu itab lefo rsm all-s cale appl icat ion sw her elo cal vari abili tyc anb em anag ed;m odel para met eriz atio na nd test ing can be don e;co llect ion ofl anduse and veri fied act ivit yda tao btai ned ;sy stem sw illb en eede dto mak ead van ced mod ellin gap proa ches acce ssib leto pro ject deve lope rsSa mpl ing and Mea sure men tH igh estd ata requ irem ents ;cos tly tomea sure an dva riab ility hig h;l ong sam plin gin terv als and cred itin gpe riod sfo rso ilca rbon ;can hav ebe stprec isio nSi tes cale ;may be subdaily ifmic rom eteo rolo gica lte chn ique sar eus edto est imat en earcon tin uous gas emis sion rat es, ore very few yea rsw ith soi lcar bon stoc kch ange ;un cert ain tyc anb eh igh ifn ota pplie dco rrec tlyL evel ofe rror sm ayb ecom eov erw hel min gin sit es/ proj ects wit hh igh var iabi lity wit hou ttig hts ampl ing and stat isti cald esig n;c anb em ost cost lyto impl emen tsubject to significant error. The Intergovernmental Panel on Climate Change (IPCC) classifies quantification methods into three tiers. For fertilizer-induced N2O emissions, the Tier 1 method supposes that 1% of applied nitrogen (N) is emitted as N2O-N, regardless of soil and climate characteristics. Detailed Tier 3 methods incorporating process-based soils emissions models such as DAYCENT (Del Grosso et al. 2006) and DNDC (Li et al. 1994) provide more refined estimates, but require a more indepth understanding of emissions processes to operate and interpret. The fertilizer-induced emissions model of Bouwman et al. (2002) is a good illustration of a Tier 2 model. It acknowledges that emissions vary as a function of soil texture, soil pH, soil drainage, climate, etc., but it only requires broad characterisations of these variables-for example soil or climate classes-as inputs. So it provides some refinement over Tier 1 methods but has the potential to reach a wider user base than Tier 3 methods. In this chapter we describe the development and application of an agricultural GHG emission calculator called the Cool Farm Tool, which can be viewed within this framework as an integrated Tier 2 model. We give a concise overview of the first version of the tool (V1.0), and then describe several new features in an upcoming release. Use of the tool will be demonstrated in a study of Kenyan smallholder coffee production.