32:T407,md`In this notebook we try to examine potential relationships between the soil quality and soil moisture on a crops yield in a specific year. For the analysis, [crop yields](https://www.landesdatenbank.nrw.de/ldbnrw//online?operation=table&code=41241-01iz&bypass=true&levelindex=1&levelid=1611304706790) (2013-2018) as well as the [total cultivation areas](https://www.landesdatenbank.nrw.de/ldbnrw//online?operation=table&code=41271-03iz&bypass=true&levelindex=1&levelid=1611304625605) (2016) were queried for all NUTS3 regions (*Kreise*) in NRW. Furthermore, soil moisture indeces ([DFN](https://www.ufz.de/index.php?en=37937), at 25 cm and 1.8 m depth) were acquired for the corresponding regions and years. Together with the soil quality indicators *Soil Quality Rating*, (SQR, [BGR Kartenwerk SQR1000](https://www.bgr.bund.de/DE/Themen/Boden/Ressourcenbewertung/Ertragspotential/Ertragspotential_node.html)), and *Bodenpunkte* ([BK 50](https://www.gd.nrw.de/pr_od_ibk50.htm)), the data was accumulated and reshaped in *R*.`33:T9b6,cropYields_timeline = { let c = DOM.canvas(width < 500 ? width : 500, width < 500 ? width : 400); var lineChart = new Chart(c.getContext("2d"), { type: "line", data: { labels: [...new Array(20)].map((n, i) => 1999 + i), datasets: Object.keys(cropsAndMatchings).map((ktbl, i) => { const cropData = cropsAndMatchings[ktbl]; const crop = cropData.yieldName; const yieldData = yield_data_ags .filter( (r) => r.nuts3 === nuts3.find((f) => f.properties.na === "Gütersloh").properties.id ) //'DEA42') .map((r) => r[cropsAndMatchings[ktbl].yieldName]); return { label: crop, yAxisID: highYieldCrops.indexOf(crop) > -1 ? "y1" : "y", fill: false, backgroundColor: hexToRgbA(d3.schemeTableau10[i], 0.7), borderColor: d3.schemeTableau10[i], data: yieldData, borderWidth: 3, radius: 4, pointBackgroundColor: "white" }; }) }, options: { responsive: false, scales: { y: { type: "linear", position: "left" }, y1: { type: "linear", display: true, position: "right" } }, plugins: { datalabels: false } } }); viewof secondYaxis.addEventListener("input", async () => { lineChart.options.scales.y1.display = !lineChart.options.scales.y1.display; Object.keys(cropsAndMatchings).map((ktbl, i) => { const cropData = cropsAndMatchings[ktbl]; const crop = cropData.yieldName; if (highYieldCrops.indexOf(crop) === -1) return; lineChart.data.datasets[i].yAxisID = lineChart.data.datasets[i].yAxisID === "y1" ? "y" : "y1"; }); lineChart.update(); }); viewof nuts3_selected.addEventListener("input", async () => { const selected_nuts3_reg = viewof nuts3_selected.value; Object.keys(cropsAndMatchings).map((ktbl, i) => { const cropData = cropsAndMatchings[ktbl]; const crop = cropData.yieldName; const yieldData = yield_data_ags .filter( (r) => r.nuts3 === nuts3.find((f) => f.properties.na === selected_nuts3_reg).properties .id ) //'DEA42') .map((r) => r[cropsAndMatchings[ktbl].yieldName]); lineChart.data.datasets[i].data = yieldData; }); lineChart.update(); }); return c; }34:T66f,function coefficientPlot(model) { let correction = 1 if (model.modelWithoutIntercept) correction = 0 const preparedData = model.independantVars.map((variable,i) => { return { name: variable.string, coefficient: model.coefficients[i + correction], standardErrorStart: model.coefficients[i + correction] - (model.standardErrors[i + correction] / 2), standardErrorStop: model.coefficients[i + correction] + (model.standardErrors[i + correction] / 2), } }) const red = "#C43228" const blue = "#436FA1" return embed({ "$schema": "https://vega.github.io/schema/vega-lite/v5.json", "data": {"values": preparedData}, "encoding": {"y": {"field": "name", "type": "ordinal", "title": ""}}, "layer": [ { "mark": {"type": "rule"}, "encoding": { "x": {"field": "standardErrorStart", "type": "quantitative", "title": "Estimate"}, "x2": {"field": "standardErrorStop", "type": "quantitative", "title": "Estimate"}, "y": {"field": "name", "type": "ordinal"}, "color": { "condition": { "test": "datum['coefficient'] < 0", "value": red }, "value": blue } } }, { "mark": {"type": "point", "filled": true}, "encoding": { "x": { "field": "coefficient", "type": "quantitative", "scale": {"zero": false}, "title": "Estimate" }, "color": { "condition": { "test": "datum['coefficient'] < 0", "value": red }, "value": blue } } } ] }) }35:T2163,function stargazer(models, options = {}) { if (!models) throw TypeError("No model specified."); if (!Array.isArray(models)) models = [models]; const columns = models.length + 1; const columnsArray = [...new Array(columns)]; function checkPValue(value) { if (value < 0.01) { return "***"; } else if (value < 0.05) { return "**"; } else if (value < 0.1) { return "*"; } else { return ""; } } function pad(string) { if (!(string.startsWith("-") || string.startsWith("("))) { string = " " + string; } const curLength = string.length; const desired = 9; while (string.length < 9) { string += " "; } return string; } let htmlString = ` `; const depedentVars = {}; for (let i = 0; i < models.length; ++i) { let label = models[i].dependantVar.string; if (options.labels && options.labels[label]) { label = options.labels[label]; } if (depedentVars[label]) depedentVars[label] += 1; else depedentVars[label] = 1; } const indepdendantVars = {}; for (let i = 0; i < models.length; ++i) { const curModel = models[i]; for (let j = 0; j < curModel.independantVars.length; ++j) { const predictor = curModel.independantVars[j].string; if (options.labels && options.labels[predictor]) { if (!indepdendantVars[options.labels[predictor]]) { indepdendantVars[options.labels[predictor]] = [predictor]; } else if ( indepdendantVars[options.labels[predictor]].indexOf(predictor) === -1 ) { indepdendantVars[options.labels[predictor]].push(predictor); } } else if (!indepdendantVars[predictor]) { indepdendantVars[predictor] = [predictor]; } } } // const indepdendantVars = [...new Set(models.reduce((arr,cur) => (cur.independantVars.forEach(v => arr.push(v.string)), arr),[]))] // ToDo implement optional sorting htmlString += `

${Object.keys(depedentVars) .map( (key, i) => `` ) .join("")} ${ options.modelTypes ? Object.keys(options.modelTypes) .map((key, i) => { return ``; }) .join("") : Object.keys(depedentVars) .map( (key, i) => `` ) .join("") } ${models .map( (m, i) => `` ) .join("")} ${Object.keys(indepdendantVars) .map( (vLabel) => ` ${models .map((m) => { // find the index of the variable in the coefficients and std. error table const possibleNames = indepdendantVars[vLabel]; let varIndex = m.independantVars.findIndex( (v) => possibleNames.indexOf(v.string) > -1 ); if (varIndex !== -1 && !m.modelWithoutIntercept) varIndex = varIndex + 1; if (varIndex > -1) { return ``; } else { return ""; } }) .join("")} ` ) .join("")} ${ models.some((m) => !m.modelWithoutIntercept) && `` + models .map((m) => { if (!m.modelWithoutIntercept) { return ``; } else { return ""; } }) .join("") + "" } ${models .map( (m, i) => `` ) .join("")} ${models .map( (m, i) => `` ) .join("")} ${models .map( (m, i) => `` ) .join("")} ${models .map( (m, i) => `` ) .join("")} ${models .map( (m, i) => `` ) .join("")}

	Depedent variable:
	${ options.labels && options.labels[key] ? options.labels[key] : key }
	${key}	OLS
	${ options.modelNames ? options.modelNames[i] + " " : `(${i + 1}) ` }
${vLabel}	${pad( `${m.coefficients[varIndex].toFixed(3)}${checkPValue( m.pValues[varIndex] )}` )} ${pad( `(${m.standardErrors[varIndex].toFixed(3)})` )}
Constant	${pad( `${m.coefficients[0].toFixed(3)}${checkPValue( m.pValues[0] )}` )} ${pad(`(${m.standardErrors[0].toFixed(3)})`)}
Observations	${( m.rdf + 2 ).toString()}
R²	${m.rSquared.toFixed( 3 )}
Adjusted R²	${m.adjRSquared.toFixed( 3 )}
Residual Std. Error	${m.s.toFixed( 3 )}(df = ${m.rdf})
F Statistic	${m.f.toFixed( 3 )}${checkPValue(m.f)}(df = ${m.dfm};${m.rdf})
Note:	p<0.1; p<0.05; **p<0.01

`; return html`${htmlString}`; //return depedentVars }36:Tdee,cropData = Object.keys(regResults).map((k, i) => { const minYear = 2010; const price = Math.round( d3.mean( pricesYieldsDirectCosts[k] .filter((r) => r.region === "Nordrhein-Westfalen" && r.year > minYear) .map((year) => year.price) ) * 100 ) / 100; const lowYield = Math.round((regResults[k][0] + regResults[k][1] * 30) * 100) / 100; const highYield = Math.round((regResults[k][0] + regResults[k][1] * 100) * 100) / 100; const directCosts = Math.round( (d3.mean( pricesYieldsDirectCosts[k] .filter((r) => r.region === "Nordrhein-Westfalen" && r.year > minYear) .map((year) => year.seeds) ) + d3.mean( pricesYieldsDirectCosts[k] .filter( (r) => r.region === "Nordrhein-Westfalen" && r.year > minYear ) .map((year) => year.fertilizer) ) + d3.mean( pricesYieldsDirectCosts[k] .filter( (r) => r.region === "Nordrhein-Westfalen" && r.year > minYear ) .map((year) => year.pesticides) ) + d3.mean( pricesYieldsDirectCosts[k] .filter( (r) => r.region === "Nordrhein-Westfalen" && r.year > minYear ) .map((year) => year.others) )) * 100 ) / 100; const months = [ "JAN", "FEB", "MRZ", "APR", "MAI", "JUN", "JUL", "AUG", "SEP", "OKT", "NOV", "DEZ" ]; const lowVarCosts = Math.round(predict(regCoefficients[k].varCosts, 20, 1) * 100) / 100; const highVarCosts = Math.round(predict(regCoefficients[k].varCosts, 1, 30) * 100) / 100; const lowFixCosts = Math.round(predict(regCoefficients[k].fixCosts, 20, 1) * 100) / 100; const highFixCosts = Math.round(predict(regCoefficients[k].fixCosts, 1, 30) * 100) / 100; const laborCostsMin = Math.round( months.reduce((sum, month) => { sum += predict(regCoefficients[k][month], 20, 1) * 13.5; return sum; }, 0) * 100 ) / 100; const laborCostsMax = Math.round( months.reduce((sum, month) => { sum += predict(regCoefficients[k][month], 1, 30) * 13.5; return sum; }, 0) * 100 ) / 100; const demcials = 1; return { Crop: cropsEng[i], "Price (€/dt)": price, "Yield (dt/ha)": `${lowYield.toFixed(2)}
${highYield.toFixed(2)}`, "Direct costs (€/ha)": directCosts, "Variable machine costs (€/ha)": `${lowVarCosts.toFixed( demcials )}
${highVarCosts.toFixed(demcials)}`, "Gross margin (€/ha)": `${( Math.round((lowYield * price - directCosts - highVarCosts) * 100) / 100 ).toFixed(demcials)}
${( Math.round((highYield * price - directCosts - lowVarCosts) * 100) / 100 ).toFixed(demcials)}`, "Fix machine costs (€/ha)": `${lowFixCosts.toFixed( demcials )}
${highFixCosts.toFixed(demcials)}`, "Fix labor costs": `${laborCostsMin.toFixed( demcials )}
${laborCostsMax.toFixed(demcials)}`, "Profit (€/ha)": `${( Math.round( (lowYield * price - directCosts - highVarCosts - highFixCosts - laborCostsMax) * 100 ) / 100 ).toFixed(demcials)}
${( Math.round( (highYield * price - directCosts - lowVarCosts - lowFixCosts - laborCostsMin) * 100 ) / 100 ).toFixed(demcials)}` }; })37:T533b,regCoefficients = ({ "konventionell/integriert::Mais - Körnermais::wendend, gezogene Saatbettbereitung, Saat": { varCosts: [ 429.2100054682, -2.15585823294687, 0.0113375527390382, 4.67166502967974, -0.0335173702325071, 0.0108466768551407 ], fixCosts: [ 404.644339578215, -2.99848438883808, 0.0126061380285447, 3.57776575875562, -0.030297488509476, 0.0154255499402457 ], diesel: [ 87.6239368617408, -0.970760779480302, 0.00596949331439337, 2.51384452879148, -0.0162398685694236, 0.00415596692077977 ], kea: [ 40012.9253842185, -63.8546762633683, 0.408415311367651, 143.360060954624, -0.962513851783179, 0.282736820138856 ], JAN: [0, 0, 0, 0, 0, 0], FEB: [0, 0, 0, 0, 0, 0], MRZ: [ 0.22692954207248, -0.0135910629678798, 0.000262785707520569, 0.000888304021087536, -0.00000830077546462287, 0.0000543155715838675 ], APR: [ 3.37830550032205, -0.245688387844083, 0.00497703379193492, 0.138751083557847, -0.00128187705085698, -0.000338751739448481 ], MAI: [ 1.21190964874099, -0.130956786070422, 0.00278531145619039, 0.0701569374534761, -0.00154141509850227, -0.00039902409974463 ], JUN: [0, 0, 0, 0, 0, 0], JUL: [0, 0, 0, 0, 0, 0], AUG: [0, 0, 0, 0, 0, 0], SEP: [ 0.0405518619090656, -0.00246468213684533, 0.0000416472772457151, -0.0000707759058325412, 0.0000164162002016214, -0.00000126577335086288 ], OKT: [ 5.38588065725296, -0.255561344376825, 0.00491118752527451, 0.052414550981943, -0.000110431239667109, 0.00047910830338324 ], NOV: [0, 0, 0, 0, 0, 0], DEZ: [0, 0, 0, 0, 0, 0] }, "konventionell/integriert::Mais - Silomais::wendend, gezogene Saatbettbereitung, Saat": { varCosts: [ 288.61946201224, -2.58538745487464, 0.0296063471497184, 8.73564588745984, -0.0286179805909541, 0.0082784553117506 ], fixCosts: [ 437.163179799918, -3.34313506866754, 0.0344412745721111, 5.05168734391968, -0.0207417334627374, 0.011163571390955 ], diesel: [ 112.572157112851, -0.888145894983862, 0.0113598812775828, 4.89880257350034, -0.013457765342837, 0.00287077077298065 ], kea: [ 24739.034083713, -60.7160778768697, 0.737350862886564, 271.943963027296, -0.789043537305895, 0.199455361374704 ], JAN: [0, 0, 0, 0, 0, 0], FEB: [0, 0, 0, 0, 0, 0], MRZ: [ 0.588496131265402, -0.0159504469622687, 0.000159792425476146, 0.005363130105016, -0.000049592021256955, 0.0000894831390730908 ], APR: [ 2.34154980979784, -0.0619131178252382, 0.000712212215391115, 0.185397149034153, -0.000842321160342509, -0.0000980702036055508 ], MAI: [ 0.917036063378869, -0.0457085080137068, 0.000523417711629952, 0.0595216962959223, -0.000794446029824033, 0.0000500303773403446 ], JUN: [0, 0, 0, 0, 0, 0], JUL: [0, 0, 0, 0, 0, 0], AUG: [0, 0, 0, 0, 0, 0], SEP: [ 5.67005733158739, -0.043353573490777, 0.00048151079202749, 0.295059981948936, -0.000382905979338823, -0.000193192443448916 ], OKT: [ 2.11388751950463, -0.0452229837584191, 0.000459326861547127, 0.0341059140503916, -0.000275122701714047, 0.00060718394778087 ], NOV: [0, 0, 0, 0, 0, 0], DEZ: [0, 0, 0, 0, 0, 0] }, "konventionell/integriert::Sommerackerbohnen - Körnergewinnung::wendend, gezogene Saatbettbereitung, Saat": { varCosts: [ 215.863152415406, -3.78439473921307, 0.0414005836025691, 3.77493073611044, -0.0501804659100928, 0.0122365014849467 ], fixCosts: [ 291.051285992337, -4.72975321197313, 0.0493387611154054, 3.09133979614978, -0.036893166109697, 0.0161695748292632 ], diesel: [ 94.7093042736429, -1.69166553165659, 0.0187501224904081, 1.90787422271873, -0.0256058957774357, 0.00488041474490115 ], kea: [ 14575.1519593357, -106.052817199728, 1.16401917870142, 110.754459242634, -1.47280110314596, 0.324378005353494 ], JAN: [0, 0, 0, 0, 0, 0], FEB: [0, 0, 0, 0, 0, 0], MRZ: [ 2.80509595582486, -0.146695350409434, 0.00156690523636225, 0.108294573638782, -0.00123831574877968, -0.00061945243526232 ], APR: [0, 0, 0, 0, 0, 0], MAI: [ 0.769781312041604, -0.0529379530852461, 0.000585216504612539, 0.0546266750575244, -0.000640613220174498, -0.000406597232317381 ], JUN: [0, 0, 0, 0, 0, 0], JUL: [0, 0, 0, 0, 0, 0], AUG: [ 1.86633079569951, -0.0572765991525298, 0.000582771489512085, 0.0236532608257089, -0.000127973685059161, -0.000084942909915301 ], SEP: [ 0.925680932233967, -0.0309942393637948, 0.000308753108504588, 0.00952975868691227, -0.0000713385421707318, 0.0000301986540459087 ], OKT: [ 2.48797194677527, -0.0598464953546542, 0.000577357212751559, 0.0101571955146666, 0.0000730323756242483, 0.000691150483280152 ], NOV: [0, 0, 0, 0, 0, 0], DEZ: [0, 0, 0, 0, 0, 0] }, "konventionell/integriert::Sommerhafer - Futterhafer::wendend, gezogene Saatbettbereitung, Saat": { varCosts: [ 223.352174725934, -3.73445491728783, 0.0409605987018879, 3.81758923640492, -0.0502411542067424, 0.0126684889587166 ], fixCosts: [ 314.026373677689, -4.66377527348759, 0.0487835540095602, 3.08991015320403, -0.0371314980782127, 0.0172096701260336 ], diesel: [ 86.4981335881933, -1.63204416499057, 0.0182025822619165, 1.92503707852753, -0.02553604075011, 0.00496053212597993 ], kea: [ 18927.5996108156, -103.295687163159, 1.13916327908862, 111.942348984927, -1.47185557058623, 0.33203729863658 ], JAN: [0, 0, 0, 0, 0, 0], FEB: [ 0.911029218981001, -0.0336426565326641, 0.000326857146119472, -0.00224279447740357, 0.0000599672564784631, 0.000140832735623516 ], MRZ: [ 2.22021344563609, -0.121910294723029, 0.0013263089980723, 0.114244454232673, -0.00129665826544357, -0.00069360015968067 ], APR: [0, 0, 0, 0, 0, 0], MAI: [ 0.922018191148241, -0.0563772882811788, 0.000618882939443212, 0.0551257268439698, -0.000638361768765848, -0.000358484189139772 ], JUN: [0, 0, 0, 0, 0, 0], JUL: [ 2.03454869230121, -0.0542375159258479, 0.000553215157764544, 0.023952299997034, -0.000121999008264246, -0.0000951781429475501 ], AUG: [ 0.889447025890532, -0.0297407717149164, 0.000297480036277098, 0.00953291986943858, -0.0000797988233125156, 0.0000320827745676871 ], SEP: [ 0.840922616417743, -0.0230837728327669, 0.000220572736116028, 0.000852663988910213, 0.000041238412756157, 0.0000976240432064039 ], OKT: [ 1.67295079532968, -0.0383323463296036, 0.000370750347153308, 0.00741434579346867, 0.0000411370348450032, 0.000603910225836226 ], NOV: [0, 0, 0, 0, 0, 0], DEZ: [0, 0, 0, 0, 0, 0] }, "konventionell/integriert::Speisekartoffeln::wendend, gezogene Saatbettbereitung, Legen": { varCosts: [ 576.916232704976, -5.76683849446926, 0.0244388350460594, 12.1929184853739, -0.133614262540657, 0.0254483265509225 ], fixCosts: [ 1665.51750558769, -3.16179448029805, 0.00190261642242552, 6.8367625586126, -0.0780483204135041, 0.0202471854432404 ], diesel: [ 162.768024824738, -2.75924891172712, 0.0242116646056008, 5.83661645227241, -0.0593089539051029, 0.00775961612096052 ], kea: [ 73654.1216677845, -154.472056557787, 1.20663437362338, 328.236961432709, -3.3860899910726, 0.484798155854141 ], JAN: [0, 0, 0, 0, 0, 0], FEB: [0, 0, 0, 0, 0, 0], MRZ: [ 1.17957280224013, -0.079929519367936, 0.0015115019092851, 0.00613733029249511, 0.000124723888595082, 0.00013393150500216 ], APR: [ 2.98064473564797, -0.196231741051085, 0.00400348915682948, 0.0755351722596071, -0.00130823734813246, -0.000245107651497188 ], MAI: [ 1.13398353974857, -0.130996539370165, 0.00279290613311675, 0.0680197996058267, -0.00157699466055836, -0.000419560952892815 ], JUN: [ 2.09891219808761, -0.24571925294752, 0.00527931166258434, 0.136798938288498, -0.00317442073623243, -0.00085270805213522 ], JUL: [ 3.14836829713141, -0.36857887942128, 0.00791896749387651, 0.205198407432747, -0.00476163110434864, -0.00127906207820283 ], AUG: [ 3.65368518567314, -0.314929384981467, 0.00657449541336038, 0.154673380860998, -0.00300000134524342, -0.000787091071542103 ], SEP: [ 19.3646179578112, -0.419146629763107, 0.00753253979763221, 0.167900698822457, 0.000613390051517383, 0.000848422349102642 ], OKT: [ 0.153347791015261, -0.0173863436332863, 0.000377219525671576, 0.011026096626999, -0.000261079308896772, -0.0000697406170114126 ], NOV: [ 2.66074103975105, -0.104207657999445, 0.00195975202413479, 0.0106252577752928, 0.0000871668906268252, 0.000698704535683268 ], DEZ: [0, 0, 0, 0, 0, 0] }, "konventionell/integriert::Wintergerste - Futtergerste::wendend, gezogene Saatbettbereitung, Saat": { varCosts: [ 239.521202815493, -4.20296470220125, 0.0464493574290804, 4.66752115938594, -0.0602111198285396, 0.0128575601186178 ], fixCosts: [ 337.814635465614, -4.99017721647125, 0.0525331328602185, 3.64425757340994, -0.0437494007004305, 0.0178395859929732 ], diesel: [ 93.2566690394394, -1.8286392597914, 0.0205252294226285, 2.33856110776134, -0.0300582492951727, 0.00502843730045007 ], kea: [ 24554.5582100786, -114.945417281849, 1.27639514407921, 135.523781635802, -1.73528012005295, 0.338904962668196 ], JAN: [0, 0, 0, 0, 0, 0], FEB: [ 0.476543540631448, -0.0124689199557526, 0.000122223044178254, 0.00325665386578592, 0.000015915336941932, 0.000115034366670089 ], MRZ: [ 0.152236879106639, -0.00343933519593268, 0.0000336664348306742, 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0.0546822898123803, -0.000633705103301915, -0.000351345007326354 ], NOV: [0, 0, 0, 0, 0, 0], DEZ: [0, 0, 0, 0, 0, 0] }, "konventionell/integriert::Winterweizen - Brotweizen::wendend, gezogene Saatbettbereitung, Saat": { varCosts: [ 250.603812159147, -4.80742676988043, 0.0533326531545913, 5.49059868432696, -0.0709168324257832, 0.0128756664627659 ], fixCosts: [ 345.530526812257, -5.49293792795201, 0.0580665275079372, 4.18466747869059, -0.0508956801971134, 0.0184206946744004 ], diesel: [ 99.4363312466489, -2.11521320530601, 0.0237602199415039, 2.73167810266246, -0.0350488662101353, 0.00505831117165674 ], kea: [ 29538.4849528999, -131.767650612067, 1.46607508537303, 158.18271303612, -2.0234958979245, 0.340770820945803 ], JAN: [0, 0, 0, 0, 0, 0], FEB: [ 0.476543540631448, -0.0124689199557526, 0.000122223044178254, 0.00325665386578592, 0.000015915336941932, 0.000115034366670089 ], MRZ: [ 0.152236879106639, -0.00343933519593268, 0.0000336664348306742, 0.000499051786445538, 0.00000225145140864962, 0.0000481130431776055 ], APR: [ 1.66040787396335, -0.108833116642656, 0.00119892849690033, 0.112335567946581, -0.00126675190149291, -0.000804149944450452 ], MAI: [ 0.152236879106639, -0.00343933519593268, 0.0000336664348306742, 0.000499051786445538, 0.00000225145140864962, 0.0000481130431776055 ], JUN: [ 0.885900086484899, -0.0558399217598458, 0.000612560824062155, 0.055965774280988, -0.000625562086600701, -0.000399903155305928 ], JUL: [0, 0, 0, 0, 0, 0], AUG: [ 2.99381056665741, -0.0805638664132853, 0.000808173408411084, 0.0371841924810517, -0.000126478796273685, -0.0000523833253356581 ], SEP: [ 2.5243172170435, -0.061087604487708, 0.00058886007840756, 0.0109653949923662, 0.0000813922459164599, 0.000681349582490185 ], OKT: [ 2.80325389819325, -0.146686272258406, 0.00156690489810722, 0.108376006068021, -0.0012422271052776, -0.00062521879412018 ], NOV: [0, 0, 0, 0, 0, 0], DEZ: [0, 0, 0, 0, 0, 0] }, "konventionell/integriert::Zuckerrüben::wendend, gezogene Saatbettbereitung, Saat": { varCosts: [ 271.530125624239, -2.18436158074082, -0.000415913422633035, 3.49165121689464, -0.0460216625736485, 0.0126045332158055 ], fixCosts: [ 286.387751066987, -2.19644340200376, 0.000438198718503242, 2.82056743080679, -0.0360317236629468, 0.0152187413258345 ], diesel: [ 110.586340401257, -0.822990682803323, 0.000271630204565298, 1.494244559591, -0.0200278039206002, 0.0044064918510112 ], kea: [ 25165.9837915141, -54.3763586097906, 0.0660234144229369, 89.8387369820858, -1.19425395253681, 0.295853866743197 ], JAN: [0, 0, 0, 0, 0, 0], FEB: [ 0.22692954207248, -0.0135910629678798, 0.000262785707520569, 0.000888304021087536, -0.00000830077546462287, 0.0000543155715838675 ], MRZ: [ 2.69311315104058, -0.227582177678629, 0.00460022178410461, 0.0709846258340449, -0.00137205617333593, -0.000276834526814349 ], APR: [0, 0, 0, 0, 0, 0], MAI: [ 0.913257056089143, -0.116947817035455, 0.00252295633557566, 0.0672440325308105, -0.00157176960163051, -0.000466896898294048 ], JUN: [0, 0, 0, 0, 0, 0], JUL: [ 0.164591086563071, -0.00740766654543324, 0.000140004744360398, 0.000624597556536038, 5.84091229300822e-7, 0.0000477204567641528 ], AUG: [ 0.913257056089143, -0.116947817035455, 0.00252295633557566, 0.0672440325308105, -0.00157176960163051, -0.000466896898294048 ], SEP: [ 1.64892259907917, -0.0444757947058448, 0.000802213304363352, 0.00217481009091996, 0.000111731357910611, 0.0000488164192077988 ], OKT: [ 2.81370177812193, -0.147596418665282, 0.00282487041957174, 0.0261647960378014, -0.000129087395372112, 0.000588887909430285 ], NOV: [0, 0, 0, 0, 0, 0], DEZ: [0, 0, 0, 0, 0, 0] } 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30:{"title":"Influence of soil quality and soil moisture index on crop yields","cells":[{"id":0,"value":"md`# Influence of soil quality and soil moisture index on crop yields`","pinned":false,"hidden":false,"mode":"ojs"},{"id":370,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":279,"value":"full_dataset = FileAttachment('full_dataset.csv').csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":537,"value":"data_with_mais = full_dataset.map(r => {\n Object.keys(r).forEach(key => {\n if (key.includes('Silomais/Grünmais (einschl. Lieschkolbenschrot)')) {\n r[\n key.replace('Silomais/Grünmais (einschl. Lieschkolbenschrot)', 'Mais')\n ] = r[key];\n }\n });\n return r;\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":409,"value":"md`The remainder of the notebook focuses on a single crop and soil quality indicator.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":34,"value":"viewof crop = Select(crops, { value: \"Winterweizen\", label: \"Select a crop\" })","pinned":false,"hidden":false,"mode":"ojs"},{"id":43,"value":"viewof indicator = Select(['bodenzahl', 'sqr'], {\n value: \"bodenzahl\",\n label: \"Select a soil quality indicator\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":413,"value":"md`Before we proceed, we remove outliers (regarding crop yield) from the dataset using the interquartile range ${tex`(df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))`}.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":13,"value":"cleaned = cleanIQR(full_dataset, [`${crop}_yield`, `${crop}_${indicator}`])","pinned":true,"hidden":false,"mode":"ojs"},{"id":398,"value":"md`In order to get a better overview of the dataset, we create a pairs plot of the ${crop} yield, ${indicator}, and soil moisture index (SMI).`","pinned":false,"hidden":false,"mode":"ojs"},{"id":354,"value":"yield_soilQuality_smi_plot = pairplot(cleaned, {\n vars: [`${crop}_yield`, `${crop}_${indicator}`, \"smi\"],\n width: 800,\n height: 600,\n hue: \"year\",\n hueVarType: \"nominal\",\n markers: true\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":744,"value":"md`## Development of yields at regional level`","pinned":false,"hidden":false,"mode":"ojs"},{"id":748,"value":"viewof nuts3_selected = select({\n title: \"Kreis / Landkreis\",\n description: \"Wähle einen Landkreis aus\",\n options: nuts3.map((f) => f.properties.na),\n value: \"Gütersloh\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":754,"value":"viewof secondYaxis = toggleSwitch({\n textOn: \"2nd Y-Axis: On\",\n textOff: \"2nd Y-Axis: Off\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":760,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":773,"value":"md`### ${crop} yield (dt/ha). Olympic mean 2013-2018.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":786,"value":"legend({\n color: d3.scaleSequential([minYield, maxYield], d3.interpolateGreens)\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":778,"value":"yieldMap = vl\n .markGeoshape({ stroke: \"white\" })\n .data(choroplethData)\n .encode(\n vl\n .color({\n condition: {\n test: `!datum['properties.${selected_ktbl}_yield']`,\n value: \"lightgrey\"\n }\n })\n .fieldQ(`properties.${selected_ktbl}_yield`)\n .scale({\n scheme: \"greens\"\n })\n .legend(null),\n vl.tooltip([\n {\n field: \"properties.na\",\n type: \"nominal\",\n title: \"Kreis\"\n },\n {\n field: `properties.${selected_ktbl}_yield`,\n type: \"quantitative\",\n title: \"Ertrag (dt/ha)\"\n },\n {\n field: `properties.${selected_ktbl}_sqr`,\n type: \"quantitative\",\n title: \"Soil qualiy rating (SQR)\"\n }\n ])\n )\n .width(width < 400 ? width : 400)\n .height(300)\n .config({\n view: { stroke: null }\n })\n .render()","pinned":false,"hidden":false,"mode":"ojs"},{"id":788,"value":"minYield = d3.min(\n choroplethData.map(p => p.properties[selected_ktbl + \"_yield\"])\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":791,"value":"maxYield = d3.max(\n choroplethData.map((p) => p.properties[selected_ktbl + \"_yield\"])\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":780,"value":"selected_ktbl = Object.keys(cropsAndMatchings).find(\n (c) => cropsAndMatchings[c].yieldName === crop\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":742,"value":"import {\n nuts3,\n cropsAndMatchings,\n yield_data_ags,\n highYieldCrops,\n vl,\n choroplethData,\n legend\n} from \"52b07809926d5c5d\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":423,"value":"md`### Simple linear model (WLS)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":426,"value":"md`This first model solely accounts for the influence of the ${indicator} on the ${crop} yield, without considering the SMI. The percentage of the cultivation area in a NUTS3 region of the total cultivation area of the crop in NRW is passed as a weight to each observation from the corresponding NUTS3 region.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":684,"value":"linearModel = lm(`${crop}_yield ~ log(${crop}_${indicator})`, cleaned, {\n weights: cleaned.map((r) => r[`${crop}_weight`])\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":269,"value":"summary(linearModel)","pinned":true,"hidden":false,"mode":"ojs"},{"id":691,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":437,"value":"md`### Modeling interaction between ${indicator} and SMI`","pinned":false,"hidden":false,"mode":"ojs"},{"id":706,"value":"interactionModel = lm(\n `${crop}_yield ~ ${crop}_${indicator} + smi + log(${crop}_${indicator} * smi)`,\n cleaned,\n {\n weights: cleaned.map(r => r[`${crop}_weight`])\n }\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":52,"value":"summary(interactionModel)","pinned":true,"hidden":false,"mode":"ojs"},{"id":442,"value":"md`### Considering both ${indicator} and detailed SMI`","pinned":false,"hidden":false,"mode":"ojs"},{"id":727,"value":"detailedModel = lm(\n `${crop}_yield ~ log(${crop}_${indicator}) + JAN_25 + FEB_25 + MAR_25 + APR_25 + MAI_25 + JUN_25 + JUL_25 + AUG_25 + SEP_25 + OCT_25 + NOV_25 + DEZ_25 + JAN_gesamt + FEB_gesamt + MAR_gesamt + APR_gesamt + MAI_gesamt + JUN_gesamt + JUL_gesamt + AUG_gesamt + SEP_gesamt + OCT_gesamt + NOV_gesamt + DEZ_gesamt`,\n cleaned,\n {\n weights: cleaned.map(r => r[`${crop}_weight`])\n }\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":290,"value":"summary(detailedModel)","pinned":true,"hidden":false,"mode":"ojs"},{"id":729,"value":"coefficients_detailedModel = coefficientPlot(detailedModel)","pinned":true,"hidden":false,"mode":"ojs"},{"id":806,"value":"stargazer([linearModel, interactionModel])","pinned":true,"hidden":false,"mode":"ojs"},{"id":804,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"function cleanIQR(dataframe, columns) {\n if (!Array.isArray(columns)) columns = [columns];\n\n const keys = Object.keys(dataframe[0]);\n const bounds = columns.reduce((obj, key) => {\n const values = dataframe.map(r => r[key]).filter(r => !isNaN(r));\n const Q1 = d3.quantile(values, 0.25);\n const Q3 = d3.quantile(values, 0.75);\n const IQR = Q3 - Q1;\n\n obj[key] = {\n lower: Q1 - 1.5 * IQR,\n upper: Q3 + 1.5 * IQR\n };\n return obj;\n }, {});\n\n return dataframe.filter(row => {\n const newRow = {};\n for (let i = 0; i < keys.length; i++) {\n const key = keys[i];\n if (columns.indexOf(key) > -1) {\n const val =\n row[key] > bounds[key].lower && row[key] < bounds[key].upper;\n if (!val) return; // omit row\n newRow[key] = val;\n } else {\n newRow[key] = row[key];\n }\n }\n return newRow;\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":457,"value":"import { corrplot, cor } from '2e82ed5d6839b1de'","pinned":false,"hidden":false,"mode":"ojs"},{"id":351,"value":"import { pairplot } from '@chrispahm/plotting-pairwise-relationships-in-a-dataset'","pinned":false,"hidden":false,"mode":"ojs"},{"id":337,"value":"crops = [\n \"Winterweizen\",\n \"Roggen und Wintermenggetreide\",\n \"Wintergerste\",\n \"Winterraps\",\n \"Kartoffeln\",\n \"Zuckerrüben\",\n \"Mais\",\n \"Hafer\"\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":333,"value":"import { Select } from \"@observablehq/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":607,"value":"md`## Using KTBL Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":798,"value":"md`The following KTBL dataset is on a more coarse (NUTS2) level. Essentially, we're doing a simple linear regression similar to the first one we did above.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":827,"value":"Object.keys(pricesYieldsDirectCosts).map((c) => c.split(/::/g)[1])","pinned":true,"hidden":false,"mode":"ojs"},{"id":832,"value":"cropsEng = [\n \"Fieldbeans\",\n \"Wheat\",\n \"Rye\",\n \"Barley\",\n \"Maize - Corn\",\n \"Rapeseed\",\n \"Potatoes\",\n \"Sugarbeets\",\n \"Maize - Silage\",\n \"Summer oats\"\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":865,"value":"table(cropData)","pinned":true,"hidden":false,"mode":"ojs"},{"id":841,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":872,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":870,"value":"function predict(y, size, distance) {\n return (\n y[0] +\n y[1] * size +\n y[2] * Math.pow(size, 2) +\n y[3] * distance +\n y[4] * size * distance +\n y[5] * Math.pow(distance, 2)\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":552,"value":"regResults = Object.keys(pricesYieldsDirectCosts).reduce((obj, curCrop) => {\n const minYear = 2010;\n const curCropYields = pricesYieldsDirectCosts[curCrop]\n .filter((r) => r.year > minYear)\n .map((r) => ({\n Yield: r.yield,\n Region: r.region,\n NUTS2: nuts2_sqrs.find((f) => f.NUTS_NAME === r.region)\n ? nuts2_sqrs.find((f) => f.NUTS_NAME === r.region).NUTS_CODE\n : 0,\n SQR: nuts2_sqrs.find((f) => f.NUTS_NAME === r.region)\n ? nuts2_sqrs.find((f) => f.NUTS_NAME === r.region)._mean\n : 0\n }))\n .filter((r) => r.SQR);\n\n const dataPerRegions = [...d3.group(curCropYields, (r) => r.Region)];\n const weightsPerRegion = dataPerRegions.reduce((obj, reg) => {\n obj[reg[0]] = 1 / d3.variance(reg[1].map((r) => r.Yield));\n return obj;\n }, {});\n\n const weights = curCropYields.map((r) => weightsPerRegion[r.Region]);\n\n const model = lm(`Yield ~ SQR`, curCropYields, { weights: weights });\n obj[curCrop] = model.coefficients;\n return obj;\n}, {})","pinned":false,"hidden":false,"mode":"ojs"},{"id":592,"value":"viewof ktblCrop = Select(Object.keys(pricesYieldsDirectCosts), {\n value: \"Winterweizen\",\n label: \"Select a crop\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":613,"value":"viewof year = Select([...[...Array(19).keys()].map(i => i + 2001), 'avg'], {\n value: 'avg',\n label: \"Select a year\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":577,"value":"embed({\n $schema: \"https://vega.github.io/schema/vega-lite/v5.json\",\n description:\n \"A scatterplot showing horsepower and miles per gallons for various cars.\",\n data: { values: yields },\n mark: \"point\",\n encoding: {\n x: { field: \"SQR\", type: \"quantitative\" },\n y: { field: \"Yield\", type: \"quantitative\" }\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":602,"value":"summary(lm(\"Yield ~ SQR\", yields, { weights: weights }))","pinned":true,"hidden":false,"mode":"ojs"},{"id":584,"value":"nuts2_sqrs = FileAttachment('nuts2_sqr.csv').csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":563,"value":"yields = pricesYieldsDirectCosts[ktblCrop]\n .map((r) => ({\n Yield: r.yield,\n Region: r.region,\n Year: r.year,\n NUTS2: nuts2_sqrs.find((f) => f.NUTS_NAME === r.region)\n ? nuts2_sqrs.find((f) => f.NUTS_NAME === r.region).NUTS_CODE\n : 0,\n SQR: nuts2_sqrs.find((f) => f.NUTS_NAME === r.region)\n ? nuts2_sqrs.find((f) => f.NUTS_NAME === r.region)._mean\n : 0\n }))\n .filter((r) => {\n if (!r.SQR) return;\n else if (year === \"avg\") return r;\n return year === r.Year;\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":986,"value":"weights = {\n const dataPerRegions = [...d3.group(yields, (r) => r.Region)];\n const weightsPerRegion = dataPerRegions.reduce((obj, reg) => {\n obj[reg[0]] = 1 / d3.variance(reg[1].map((r) => r.Yield));\n return obj;\n }, {});\n\n const weights = yields.map((r) => weightsPerRegion[r.Region]);\n return weights;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":549,"value":"pricesYieldsDirectCosts = FileAttachment('pricesYieldsDirectCosts.json').json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":7,"value":"d3 = require(\"d3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":544,"value":"crops.reduce((obj, curCrop) => {\n const cleanedDataset = cleanIQR(full_dataset, [\n `${curCrop}_yield`,\n `${curCrop}_${indicator}`\n ]); // .filter(r => r.year === 2018)\n const model = lm(\n `${curCrop}_yield ~ ${curCrop}_${indicator}`,\n cleanedDataset,\n {\n weights: cleanedDataset.map(r => r[`${crop}_weight`])\n }\n );\n obj[curCrop] = model.coefficients;\n return obj;\n}, {})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1020,"value":"Object.keys(pricesYieldsDirectCosts).map((c) => c)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1022,"value":"stargazer(regData, {\n modelNames: cropData.map((c) => c.Crop)\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1011,"value":"stargazer(regData.slice(5), {\n modelNames: cropData.slice(5).map((c) => c.Crop)\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1018,"value":"stargazer(regData.slice(0, 5), {\n modelNames: cropData.slice(0, 5).map((c) => c.Crop)\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1009,"value":"regData = Object.keys(pricesYieldsDirectCosts).reduce((obj, curCrop) => {\n const minYear = 2010;\n const curCropYields = pricesYieldsDirectCosts[curCrop]\n .filter((r) => r.year > minYear)\n .map((r) => ({\n Yield: r.yield,\n Region: r.region,\n NUTS2: nuts2_sqrs.find((f) => f.NUTS_NAME === r.region)\n ? nuts2_sqrs.find((f) => f.NUTS_NAME === r.region).NUTS_CODE\n : 0,\n SQR: nuts2_sqrs.find((f) => f.NUTS_NAME === r.region)\n ? nuts2_sqrs.find((f) => f.NUTS_NAME === r.region)._mean\n : 0\n }))\n .filter((r) => r.SQR);\n\n const dataPerRegions = [...d3.group(curCropYields, (r) => r.Region)];\n const weightsPerRegion = dataPerRegions.reduce((obj, reg) => {\n obj[reg[0]] = 1 / d3.variance(reg[1].map((r) => r.Yield));\n return obj;\n }, {});\n\n const weights = curCropYields.map((r) => weightsPerRegion[r.Region]);\n\n const model = lm(`Yield ~ SQR`, curCropYields, { weights: weights });\n obj.push(model);\n return obj;\n}, [])","pinned":true,"hidden":false,"mode":"ojs"},{"id":572,"value":"embed = require(\"vega-embed\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":739,"value":"Chart = require(\"https://cdnjs.cloudflare.com/ajax/libs/Chart.js/3.1.1/chart.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":750,"value":"import { select } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":756,"value":"import { toggleSwitch } from \"@chrispahm/toggle-switch-input-button\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":762,"value":"function hexToRgbA(hex, a) {\n var c;\n if (/^#([A-Fa-f0-9]{3}){1,2}$/.test(hex)) {\n c = hex.substring(1).split(\"\");\n if (c.length == 3) {\n c = [c[0], c[0], c[1], c[1], c[2], c[2]];\n }\n c = \"0x\" + c.join(\"\");\n return (\n \"rgba(\" +\n [(c >> 16) & 255, (c >> 8) & 255, c & 255].join(\",\") +\n \",\" +\n a +\n \")\"\n );\n }\n throw new Error(\"Bad Hex\");\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":59,"value":"lm = (await import(await FileAttachment(\"linear-models.esm@59.js\").url()))\n .default","pinned":true,"hidden":false,"mode":"ojs"},{"id":861,"value":"import { table } from \"@tmcw/tables/2\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":29,"value":"import { summary } from 'c3ef6d4e928e9ece'","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W38","stdlib":"1","titling":"auto"}