Food security in a world of natural resource scarcity The role of agricultural technologies

Abstract

Tables, Figures, and Boxes Tables 2.1 Area under no-till, by continent 8 3.1 Summary of technologies simulated in DSSAT and IMPACT 36 3.2 Targeted PAWs for wheat, maize, and rice 44 3.3 Ceilings of technology adoption pathways (%) 49 4.1 Effect of climate change on average maize, rice, and wheat yields, based on process-based models (DSSAT), between 2010 and 2050 (%) 57 5.1 Change in global prices of maize, rice, and wheat, between 2010 and 2050 (%) 89 5.2 Change in production, yields, and harvested area, IMPACT baseline, MIROC A1B and CSIRO A1B scenarios, selected regions, between 2010 and 2050 (%) 90 5.3 Change in hunger indicators, IMPACT baseline, selected regions, between 2010 and 2050 (%) 90 5.4 Change in world prices of wheat, rice, and maize compared to the baseline scenario, by technology, 2050 (%) 92 5.5 Change in per capita kilocalorie availability compared to the baseline scenario, by technology, 2050 (%) 100 5.6 Effects of stacked technologies on world prices of maize, rice, and wheat, compared to the baseline scenario, 2050 (%) 104 5.7 Effects of stacked technologies on global food security compared to the baseline scenario, 2050 106 Figures 3.1 Modeling system for estimation of impacts of agricultural technologies 3.2 Aggregated average organic-to-conventional crop yield ratios (OCRs) 40 4.1 Global yield impacts compared to the baseline scenario, by crop, MIROC A1B and CSIRO A1B scenarios, 2050 (%) 4.2 Global map of yield impacts for rainfed maize, heattolerant varieties, compared to baseline scenario, MIROC A1B scenario, 2050 (%) 4.3 Global map of yield impacts for rainfed maize, no-till, compared to the baseline scenario, MIROC A1B scenario, 2050 (%) 4.4 Global map of yield impacts for irrigated rice, nitrogenuse efficiency, compared to the baseline scenario, MIROC A1B scenario, 2050 (%) 60 4.5 Global yield impacts compared to the baseline scenario, by crop and cropping system, MIROC A1B scenario, 2050 (%) 61 4.6 Global yield impacts compared to the baseline scenario, by crop and cropping system, combined technologies, MIROC A1B and CSIRO A1B scenarios, 2050 (%) Box 1A Drought impact maps for maize, baseline scenario, year 2000 Box 1B Drought impact maps for maize, CSIRO A1B scenario, year 2050 Box 1C Drought impact maps for maize, MIROC A1B scenario, year 2050 Box 2 Ex ante yield benefits of drought tolerance compared to the original variety under three climate scenarios for China and the United States viii Box 3 Growing season precipitation by drought intensity compared to the baseline scenario for maize in China and the United States, 2050 (mm) 4.7 Regional yield impacts compared to the baseline scenario, by crop and cropping system, no-till, MIROC A1B and CSIRO A1B scenarios, 2050 (%) 71 4.8 Regional yield impacts compared to the baseline scenario, by crop and cropping system, integrated soil fertility management, MIROC A1B and CSIRO A1B scenarios, 2050 (%) 4.9 Regional yield impacts compared to the baseline scenario, by crop and cropping system, precision agriculture, MIROC A1B and CSIRO A1B...

Food and water gaps to 2050: preliminary results from the global food and water system (GFWS) platform

“…The effects of nitrogen fertiliser on the six major crops modelled shows the possibility of increasing crop food supplies if sufficient fertiliser is applied and if annual crop yield improvements are in excess of 0.5 % per year. Our findings are consistent with Rosegrant et al (2014) and stress the Fig. 6 The water supply deficit or surplus (billion cubic meters) for 19 countries in 2050 under options where N fertiliser was held constant at 50 kg/ha, crop productivity improvement is at about 0,25 %/year to 2050 and irrigation water was increased from 100 mm in Scenario 1 to 200 mm/ha and 400 mm/ha in Scenario 2, and 3 respectively importance of finding ways to provide fertiliser (Roberts 2009) and nutrient balance (Ciampitti and Vyn 2014) without losses and damage to the local, regional and global environmental processes.…”

When does no-till yield more? A global meta-analysis

“…combined with residue cover and crop rotation practices) (FAO, 2011). Other international organizations predict that no-till practices have the potential to increase global maize and wheat production levels by approximately 30% by 2050 (Rosegrant et al, 2014). In general, international agricultural organizations researching or promoting no-till are not recommending that farmers simply switch to no-till without also modifying other additional system-level management factors.…”

Using ICT for Remote Sensing, Crowdsourcing, and Big Data to Unlock the Potential of Agricultural Data

“…Economic and crop modelers are also increasingly taking advantage of granular data sets, especially those harmonized on high-resolution global grids in modeling analyses that explore future consequences of climate change (Nelson 2014;Rosegrant 2014 More than just pretty maps, georeferenced data can help development practitioners visualize where populations are most vulnerable, the farming systems they most depend on, the biological and geophysical constraints and risks that limit farm productivity, the investments and innovations that could raise farm productivity most sustainably, and the broader impacts of such change.…”