Assessing changes in availability of land and water for food (1960-2050):An analysis linking food demand and available resources

Future global food demand will require more land and water. We group the global population into six Gross Domestic Product groups and study changes in the availability of land and water for food in relation to demographic and nutrition transition theories. We show large differences in land and water availability between rich and poor countries. Inequality will strongly increase due to the projected large population growth in poor countries. By 2050, the richest quarter of the global population will have three times more arable land per person than the rest. Those changing diets to a more affluent consumption will be the ones with less available resources per person. More than two-thirds of global population will not have enough land to produce the food for an affluent diet by 2050. Thus, the large land and water constraints of the poor will result in significant challenges for food security than predicted in previous studies.</p

The importance of weather data in crop growth simulation models and assessment of climatic change effects

Yields of agricultural crops are largely determined by the weather conditions during the growing season. Weather data are therefore important input variables for crop growth simulation models. In practice, these data are accepted at their face value. This is not realistic. Like all measured values, are weather data subject to inaccuracies. Crop growth simulation models are sensitive to weather data used as input, so inaccuracies in weather data can affect the simulation results. The errors in weather data were estimated and their effects on the simulation results of a spring wheat crop growth simulation model were determined. Inaccuracies in weather data caused deviations in simulated yields of 10-15 %.In most weather data sets missing values occur and since crop growth models require daily data the values of the missing data have to be estimated. Several methods to estimate missing values were discussed and their effects on simulation results were studied. Large differences in quality of the estimation methods were found. Some of them resulted in deviations in simulated yields up to 30 %.Daily weather data are not always available and often average weather data are used instead. The effects of using average weather data on simulation results were studied for three sites in different climates. For all sites large deviations in simulation results were found.The increasing CO 2 concentration is affecting agricultural production in two ways: via a climatic change and via effects on assimilation and transpiration rates. The spring wheat model was used to study the overall effects of higher CO 2 levels on wheat yields in Western Europe. A temperature rise of 3 °C resulted in a yield decline, doubled CO 2 concentration in a yield increase and the combination of both in a yield increase of about 2 ton ha -1

The water footprint of food and cooking fuel:A case study of self-sufficient rural India

Water is a basic resource for food and fuelwood production. In general, people in rural areas of India consume carbohydrate rich staples with small amounts of animal foods. They mostly depend upon fuelwood for cooking. This study assesses the WFs for food and fuel consumption in rural India. The research question is: What is the green, blue and grey water footprint (WF) of food and cooking fuel consumption per province in rural India (m3/cap/year). It used the WF method for the quantification. Data on food and fuelwood consumption were derived from the National Sample Survey (2011-12). Foods were categorized into 6 groups: 1. Rice; 2. Wheat; 3. Oils and fats; 4. Milk; 5. Other animal foods; and 6. Others. Cooking fuel includes: 1. Fuelwood; 2. Kerosene and 3. LPG. Data related to WFs of food were derived from literature reviews and in case of fuelwood, the WFs were calculated for all the provinces of India. Finally, the total WF of per cap consumption is calculated by adding the WF of food and fuelwood. The result shows that there is a large variation in the green, blue and grey WFs for food consumption across the provinces of India. The average WF for food consumption is about 800 m3/cap/year and for fuelwood is 1630 m3/cap/year. Rice and wheat dominate the green, blue and grey WFs for food, with variations among the provinces. The green WF of rice is larger than the green WF of wheat, while wheat has a larger blue WF. For cooking fuel, the average WF of fuelwood is much larger than the WF of fossil based cooking fuels. The total WF for fuelwood is twice the WF for food, showing that in rural areas of developing countries, fuelwood is water intensive with large impact on freshwater resources. Future prospects of increasing consumption of animal products will increase WFs. However, if also cooking fuel is considered, switching to fossil cooking fuel lowers WFs far more and compensates the increase due to larger animal food consumption. The trends for cooking fuel found in India might also be relevant for other developing countries