We analyzed crop production, physical inputs, and land use at the country level to assess technological changes behind the threefold increase in global crop production from 1961 to 2014. We translated machinery, fuel, and fertilizer to embedded energy units that, when summed up, provided a measure of agricultural intensification (human subsidy per hectare) for crops in the 58 countries responsible for 95% of global production. Worldwide, there was a 137% increase in input use per hectare, reaching 13 EJ, or 2.6% of the world's primary energy supply, versus only a 10% increase in land use. Intensification was marked in Asia and Latin America, where input-use levels reached those that North America and Europe had in the earlier years of the period; the increase was more accentuated, irrespective of continent, for the 12 countries with mostly irrigated production. Half of the countries (28/58), mainly developed ones, had an average subsidy >5 GJ/ha/y (with fertilizers accounting for 27% in 1961 and 45% in 2014), with most of them (23/28) using about the same area or less than in 1961 (net land sparing of 31 Mha). Most of the remaining countries (24/30 with inputs <5 GJ/ha/y), mainly developing ones, increased their cropped area (net land extensification of 135 Mha). Overall, energy-use efficiency (crop output/inputs) followed a U-shaped trajectory starting at about 3 and finishing close to 4. The prospects of a more sustainable intensification are discussed, and the inadequacy of the land-sparing model expectation of protecting wilderness via intensified agriculture is highlighted.

Capture of radiation by crop canopies drives growth rate, grain set, and yield. Since the fraction of photosynthetically active radiation absorbed by green area (fAPAR g ) correlates with normalized difference vegetation index (NDVI), remote sensors have been used to monitor vegetation. With a 10-m spatial resolution and 5-d revisiting time, the recently launched Sentinel-2 satellite is a promising tool for fAPAR g monitoring. However, the available algorithm to estimate fAPAR g is based on simulations of canopy interception of several vegetation types and was never tested in field crops. Handheld sensors, such as GreenSeeker, are another alternative to estimate fAPAR g . Our objectives were (a) to test the ability of indices derived from Sentinel-2 and GreenSeeker NDVI to capture fAPAR g of wheat (Triticum aestivum L.) crops, (b) to compare these sensors' performance against the moderate resolution imaging spectroradiometer (MODIS), and (c) to compare our Sentinel-2 model estimations with the available algorithm. In wheat fields in the southwest Argentinean Pampas, on several sampling dates, we measured fAPAR g with a quantum light sensor and NDVI with a GreenSeeker. We regressed fAPAR g measurements with vegetation indices from the different sources and selected the best models. Sentinel-2 and GreenSeeker NDVI precisely estimated fAPAR g , with a performance similar to MODIS (p < .05; RMSD = 0.09, 0.11, and 0.08; R 2 = .89, .88, and .95, respectively). The available algorithm to estimate fAPAR g with Sentinel-2 yielded biased estimations, mainly in the lower range of fAPAR g . These results suggest that simple models may provide fAPAR g estimations with Sentinel-2 and GreenSeeker in wheat crops with an accuracy suitable for agricultural applications.Abbreviations: fAPAR g , fraction of photosynthetically active radiation absorbed by green area; MODIS, moderate resolution imaging spectroradiometer; mSR, modified simple ratio; mSR2, modified simple ratio 2; ND705, red-edge normalized difference; NDVI, normalized difference vegetation index; NIR, near-infrared; PAR, photosynthetically active radiation; WDRVI, wide dynamic range vegetation index.