1"use strict";(self.webpackChunkNioLandReact=self.webpackChunkNioLandReact||[]).push([[600],{600:(e,t,i)=>{i.r(t),i.d(t,{default:()=>j});i(5043);var r=i(5475),s=i(3519),o=i(1072),a=i(8602),n=i(3107),l=i(7497),c=i(579);const d="'Inter', sans-serif",h="#28a745",f="coffee-yield-cfo-disclosures",m="How a coffee yield model feeds two CFO disclosures.",u="A continuous, satellite-derived yield estimate per farm is usually pitched as a procurement tool. The same model output is also the cleanest input a coffee buyer has for two financial-risk disclosures: physical climate risk under IFRS S2 / TCFD, and Scope 3 emission intensity under SBTi FLAG. One data layer, two regulatory destinations.",p="POLICY & COMPLIANCE",y="27 APRIL 2026",g="2026-04-27",w={fontFamily:d,fontWeight:700,fontSize:"clamp(1.5rem, 2.4vw, 1.9rem)",letterSpacing:"-0.015em",color:"#000",marginTop:"2.5rem",marginBottom:"1rem"},x={fontFamily:d,fontWeight:400,fontSize:"1.1rem",lineHeight:1.7,color:"#222",marginBottom:"1.1rem"},b={color:h,textDecoration:"underline",textDecorationThickness:"1px",textUnderlineOffset:"3px"};const j=function(){const e={"@context":"https://schema.org","@type":"BlogPosting",headline:m,description:u,datePublished:g,dateModified:g,url:`https://www.weathersafe.co.uk/blog/${f}`,author:{"@type":"Organization",name:"Weathersafe",url:"https://www.weathersafe.co.uk"},publisher:{"@type":"Organization",name:"Weathersafe",url:"https://www.weathersafe.co.uk"},mainEntityOfPage:`https://www.weathersafe.co.uk/blog/${f}`,articleSection:p};return(0,c.jsxs)(n.A,{title:`${m} \u2014 Weathersafe`,description:u,canonical:`https://www.weathersafe.co.uk/blog/${f}`,ogType:"article",jsonLd:e,children:[(0,c.jsx)("section",{style:{paddingTop:"8rem",paddingBottom:"4rem"},children:(0,c.jsx)(s.A,{children:(0,c.jsx)(o.A,{className:"justify-content-center",children:(0,c.jsxs)(a.A,{lg:"8",children:[(0,c.jsx)(r.N_,{to:"/blog",style:{fontFamily:d,fontWeight:600,fontSize:"0.85rem",color:h,textDecoration:"none",letterSpacing:"0.04em"},children:"\u2190 Blog"}),(0,c.jsxs)("div",{style:{display:"flex",alignItems:"center",gap:"0.85rem",marginTop:"2rem",marginBottom:"1rem"},children:[(0,c.jsx)("span",{style:{fontFamily:d,fontWeight:600,fontSize:"0.75rem",letterSpacing:"0.06em",color:h,background:"rgba(40,167,69,0.1)",borderRadius:"999px",padding:"0.35rem 0.9rem"},children:p}),(0,c.jsx)("span",{style:{fontFamily:d,fontWeight:500,fontSize:"0.85rem",letterSpacing:"0.04em",color:"#888"},children:y})]}),(0,c.jsx)("h1",{style:{fontFamily:d,fontWeight:800,fontSize:"clamp(2.4rem, 5vw, 3.8rem)",lineHeight:1.1,letterSpacing:"-0.03em",color:"#000",marginBottom:"1.5rem"},children:m}),(0,c.jsx)("p",{style:{fontFamily:d,fontWeight:400,fontSize:"1.25rem",lineHeight:1.55,color:"#555",marginBottom:0},children:u})]})})})}),(0,c.jsx)("section",{style:{paddingBottom:"5rem"},children:(0,c.jsx)(s.A,{children:(0,c.jsx)(o.A,{className:"justify-content-center",children:(0,c.jsxs)(a.A,{lg:"8",children:[(0,c.jsx)("p",{style:x,children:"Coffee buyers running yield estimates from origin tend to send the output to one place: procurement. The procurement team needs to know what is coming through the next harvest, what to forward-contract, what to underweight. The same number is also the cleanest input a CFO disclosure team has for two regulatory reports their company is now obliged to file."}),(0,c.jsx)("p",{style:{...x,fontWeight:700,color:"#000"},children:"Procurement's signal, finance's number."}),(0,c.jsx)("h2",{style:w,children:"What IFRS S2 actually asks for at origin"}),(0,c.jsx)("p",{style:x,children:"IFRS S2 \u2014 and its EU sibling at ESRS E1-9 \u2014 requires reporting entities to disclose the anticipated financial effects of physical climate risk on their value chain. For a coffee buyer, that translates to a specific question: under realistic climate scenarios, how does the origin supply chain perform? What is the yield exposure, what is the productivity drag, what would shift in sourcing if seasonal rainfall moves a few percent or temperature climbs a degree?"}),(0,c.jsx)("p",{style:x,children:"A defensible answer requires a yield model that runs forward under different climate assumptions, at a resolution the auditor can interrogate. Generic published yield estimates applied uniformly across regions cannot do that. Origin-specific, farm-resolution yield data is the only input a CFO disclosure team can defensibly cite under IFRS S2."}),(0,c.jsx)("h2",{style:w,children:"Why most coffee yield estimates fail this test"}),(0,c.jsx)("p",{style:x,children:"Most yield estimates in commercial coffee come from one of three places: historical averages projected forward, generic AI yield models trained on broad agricultural data, or back-calculated estimates derived from intake records after harvest. Historical averages assume the past represents the future, generic AI models lack farm-level specificity, and back-calculated estimates only exist after the fact \u2014 none of those work for forward-looking disclosure under IFRS S2."}),(0,c.jsx)("p",{style:x,children:"What a CFO disclosure team needs is a yield model that runs forward at farm resolution, calibrated against years of confirmed yield data on those specific farms. Without that calibration, the disclosure reads as a hypothetical, and a sceptical auditor will treat it that way."}),(0,c.jsx)("h2",{style:w,children:"The same model, the second disclosure"}),(0,c.jsx)("p",{style:x,children:"That same model output also feeds the buyer's SBTi FLAG disclosure. Under FLAG, yield retained at origin (or yield lost) translates into the per-kg coffee emission factor flowing into Scope 3 Category 1. A farm producing 4 kg cherry per tree carries a different per-kg emission factor than the same farm producing 0.3 kg cherry per tree, because cultivation, processing and transport emissions are roughly fixed while the productive output is not. Yield retained drops the emission intensity; yield lost raises it."}),(0,c.jsx)("p",{style:x,children:"So the same continuous yield estimate, running on the same satellite and weather data, lands in two regulatory reports inside the buyer's company. The CFO disclosure team uses it for IFRS S2 / TCFD; the CSO uses it for FLAG. Neither has to commission separate analysis, and neither has to defend a different number."}),(0,c.jsx)("h2",{style:w,children:"How Weathersafe runs the yield estimation layer"}),(0,c.jsxs)("p",{style:x,children:["At every washing station running GAP77, three data streams feed the yield model: continuous satellite stress signals on every mapped farm, ",(0,c.jsx)(r.N_,{to:"/blog/why-coffee-farms-get-wrong-rain-forecast",style:b,children:"hyperlocal weather forecasts"})," at farm-cluster level, and scout-recorded farm-health observations every two weeks per farm. The model is calibrated against ",(0,c.jsx)("strong",{children:"thirteen years"}
1)," of cherry intake records reconciled to specific farms via the EPOS pipeline."]}),(0,c.jsxs)("p",{style:x,children:["The output is a per-farm yield estimate that updates as conditions change, runs forward under climate scenarios on request, and aggregates cleanly to washing station, region or country level for inclusion in either an IFRS S2 disclosure or a FLAG progress report. The bio-input intervention layer that protects the yield being forecast is in ",(0,c.jsx)(r.N_,{to:"/blog/coffee-bio-input-stocks",style:b,children:"why coffee's pesticide footprint is a reduction lever"})," \u2014 the structural sibling to this post for the CFO/CSO dual-disclosure thesis. The same calibration logic is covered in ",(0,c.jsx)(r.N_,{to:"/blog/coffee-yield-forecast-13-years-ground-truth",style:b,children:"why coffee yield forecasts need 13 years of ground truth"}),". The pest and disease prediction layer running on the same architecture is in ",(0,c.jsx)(r.N_,{to:"/blog/coffee-pest-forecasts-feedback-loop",style:b,children:"what makes a coffee pest forecast different from a hypothesis"}),". The farm-level records that anchor the calibration are covered in ",(0,c.jsx)(r.N_,{to:"/blog/farm-level-coffee-records-compliance",style:b,children:"why coffee compliance lives at farm level"}),". The integrated weather-plus-satellite-plus-scout architecture is in the ",(0,c.jsx)(r.N_,{to:"/methodology",style:b,children:"methodology"}),"."]})]})})})}),(0,c.jsx)(l.Cu,{className:"py-10 py-lg-90",style:{borderTop:"1px solid #f0f0f0",borderBottom:"1px solid #f0f0f0"},children:(0,c.jsx)(l.Cu.Content,{children:(0,c.jsx)(s.A,{children:(0,c.jsxs)(o.A,{className:"align-items-start",children:[(0,c.jsxs)(a.A,{lg:"5",style:{marginTop:"-0.75rem"},children:[(0,c.jsxs)("h2",{style:{fontFamily:d,fontWeight:800,fontSize:"clamp(2.2rem, 4vw, 3.5rem)",letterSpacing:"-0.03em",lineHeight:1.05,color:"#000",marginBottom:"2rem"},children:["One model.",(0,c.jsx)("br",{}),"Two reports."]}),(0,c.jsxs)("div",{style:{display:"flex",gap:"1rem",flexWrap:"wrap"},children:[(0,c.jsx)(r.N_,{to:"/contact-us-solution",style:{fontFamily:d,fontWeight:600,fontSize:"0.95rem",color:"#fff",background:h,borderRadius:"6px",padding:"0.75rem 1.75rem",textDecoration:"none"},children:"Talk to us"}),(0,c.jsx)(r.N_,{to:"/methodology",style:{fontFamily:d,fontWeight:600,fontSize:"0.95rem",color:"#000",background:"#fff",border:"1px solid #d0d0d0",borderRadius:"6px",padding:"0.75rem 1.75rem",textDecoration:"none"},children:"Read the methodology"})]})]}),(0,c.jsx)(a.A,{lg:"5",className:"offset-lg-2",children:(0,c.jsx)("p",{style:{fontFamily:d,fontWeight:400,fontSize:"1.55rem",lineHeight:1.4,color:"#333",marginBottom:0},children:"A continuous, farm-resolution yield estimate calibrated against thirteen years of cherry intake records."})})]})})})})]})}}}]); 2//# sourceMappingURL=600.f9471d88.chunk.js.map
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