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Christoph Pahmeyer
Agricultural economics · Thünen Institute of Farm Economics / University Bonn
Published
Edited
Jun 16, 2021
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Food Prices of India, 1994–2021Large Food Retailer Investments By Industry 2010-2021Under The InfluenceFood AccessNigeria Household: Amount spent on foodEstimating the environmental impacts of 57,000 food products, part 2Food Snobs eat at Oleana, Cambridge MAVisualizing Worldwide IPC Food Security DataZoomable Sunburst for food imports in the US from 2010 to 2017.Food supply chainLet's talk about seeds!Regional crop yields in GermanyThe 10 Most Important CropsInteractive Exploration of Wine Quality and ValuationExploring Livestock Data around the world 🐓🐑🐄
Influence of soil quality and soil moisture index on crop yields
Forest Area By Selected Countries Since 1990Magic Balls FactoryIn store displays make a significant impact on the sale of Honey Nut CheeriosTransparency in Programmatic Advertising MarketplacesAnalysis of two cereals owned by the same brand display unique characteristics and periods of success.F1 Drivers 1997 - 2023Bar Chart Race, ExplainedF1 Constructor Standings 2010-2019Asia’s Regenerative Energy Future2022 Q2 PROFIT: 🛢 vs. 👨‍💻🛢 Oil SpillsHow flights between Europe and Eastern Asia got disruptedData Visualization - Impact of Covid on US Aviation (year 2020)Final Project: What makes an airline succesful?What's the deal with airline fares?2020/W16: Food: Greenhouse Gas Emission across supply chainInventory Manager -> Product alertEU Fishing FlowWarehouse Visualization with DataVisualVisualizing wind turbines in 3D using threebox
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Agriculture
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cleaned = cleanIQR(full_dataset, [`${crop}_yield`, `${crop}_${indicator}`])
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yield_soilQuality_smi_plot = pairplot(cleaned, {
vars: [`${crop}_yield`, `${crop}_${indicator}`, "smi"],
width: 800,
height: 600,
hue: "year",
hueVarType: "nominal",
markers: true
})
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summary(linearModel)
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summary(interactionModel)
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summary(detailedModel)
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coefficients_detailedModel = coefficientPlot(detailedModel)
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stargazer([linearModel, interactionModel])
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Object.keys(pricesYieldsDirectCosts).map((c) => c.split(/::/g)[1])
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table(cropData)
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summary(lm("Yield ~ SQR", yields, { weights: weights }))
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weights = {
const dataPerRegions = [...d3.group(yields, (r) => r.Region)];
const weightsPerRegion = dataPerRegions.reduce((obj, reg) => {
obj[reg[0]] = 1 / d3.variance(reg[1].map((r) => r.Yield));
return obj;
}, {});

const weights = yields.map((r) => weightsPerRegion[r.Region]);
return weights;
}
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Object.keys(pricesYieldsDirectCosts).map((c) => c)
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stargazer(regData, {
modelNames: cropData.map((c) => c.Crop)
})
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stargazer(regData.slice(5), {
modelNames: cropData.slice(5).map((c) => c.Crop)
})
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stargazer(regData.slice(0, 5), {
modelNames: cropData.slice(0, 5).map((c) => c.Crop)
})
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regData = Object.keys(pricesYieldsDirectCosts).reduce((obj, curCrop) => {
const minYear = 2010;
const curCropYields = pricesYieldsDirectCosts[curCrop]
.filter((r) => r.year > minYear)
.map((r) => ({
Yield: r.yield,
Region: r.region,
NUTS2: nuts2_sqrs.find((f) => f.NUTS_NAME === r.region)
? nuts2_sqrs.find((f) => f.NUTS_NAME === r.region).NUTS_CODE
: 0,
SQR: nuts2_sqrs.find((f) => f.NUTS_NAME === r.region)
? nuts2_sqrs.find((f) => f.NUTS_NAME === r.region)._mean
: 0
}))
.filter((r) => r.SQR);

const dataPerRegions = [...d3.group(curCropYields, (r) => r.Region)];
const weightsPerRegion = dataPerRegions.reduce((obj, reg) => {
obj[reg[0]] = 1 / d3.variance(reg[1].map((r) => r.Yield));
return obj;
}, {});

const weights = curCropYields.map((r) => weightsPerRegion[r.Region]);

const model = lm(`Yield ~ SQR`, curCropYields, { weights: weights });
obj.push(model);
return obj;
}, [])
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lm = (await import(await FileAttachment("linear-models.esm@59.js").url()))
.default
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import { table } from "@tmcw/tables/2"
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