Geology Reports⌕ Search
Home/Archive/USGS

USGS · 70017383

Phosphate rock demand into the next century: Impact on wolld food supply

J. R. Herring·R.J. Fantel
View original source ↗Export metadata ↓

Abstract

A vital and indisputable link exists between phosphate rock and world food supply. Phosphate rock is the source of phosphorus used to make phosphatic fertilizers, essential for growing the food needed by humans in the world today and in the future. We modeled the depletion of the known reserves and reserve base (which includes reserves) of phosphate rock based on various scenarios for increasing population and future demand for phosphate. Using these scenarios, the presently known reserves will be depleted within about 50 years, and the remainder of the reserve base will be depleted within the next 100 years. For this model, we used rates of growth of demand for phosphate rock of between 1 and 1.7 percent annually. We also examined demand rates that decrease over time toward demand stasis. Growthrate scenarios that stabilize demand at the year 2100 are little different from unconstrained growth. Demand stabilization by 2025 extends the reserve base by only about 50 years. Additional considerations could affect these depletion scenarios, causing them to be substantially too high or too low. Nonetheless, the incluctable conclusion in a world of continuing phosphate demand is that society, to extend phosphate rock reserves and reserve base beyond the approximate 100 year depletion date, must find additional reserves and/ or reduce the rate of growth of phosphate demand in the future. Society must: 91) increase the efficiency of use of known resources of easily minable phosphate rock; (2) discover new, economically-minable resources; or (3) develop the technology to economically mine the vast but currently uneconomic resources of phosphate rock that exist in the world. Otherwise, the future availability of present-cost phosphate, and the cost or availability of world food will be compromised, perhaps substantially. ?? 1993 Oxford University Press.

Explore related subjects

Nonrenewable ResourcesArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J. R. Herring, R.J. Fantel. 1993. Phosphate rock demand into the next century: Impact on wolld food supply. https://doi.org/10.1007/bf02257917

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS1997

Classification of mineral deposits into types using mineralogy with a probabilistic neural network

In order to determine whether it is desirable to quantify mineral-deposit models further, a test of the ability of a probabilistic neural network to classify deposits into types based on mineralogy was conducted. Presence or absence of ore and alteration mineralogy in well-typed deposits were used to train the network. To reduce the number of minerals considered, the analyzed data were restricted to minerals present in at least 20% of at least one deposit type. An advantage of this restriction is that single or rare occurrences of minerals did not dominate the results. Probabilistic neural networks can provide mathematically sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation, they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions. They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. The training set was reduced to the presence or absence of 58 reported minerals in eight deposit types. The training set included: 49 Cyprus massive sulfide deposits; 200 kuroko massive sulfide deposits; 59 Comstock epithermal vein gold districts; 17 quartzalunite epithermal gold deposits; 25 Creede epithermal gold deposits; 28 sedimentary-exhalative zinc-lead deposits; 28 Sado epithermal vein gold deposits; and 100 porphyry copper deposits. The most common training problem was the error of classifying about 27% of Cyprus-type deposits in the training set as kuroko. In independent tests with deposits not used in the training set, 88% of 224 kuroko massive sulfide deposits were classed correctly, 92% of 25 porphyry copper deposits, 78% of 9 Comstock epithermal gold-silver districts, and 83% of six quartzalunite epithermal gold deposits were classed correctly. Across all deposit types, 88% of deposits in the validation dataset were correctly classed. Misclassifications were most common if a deposit was characterized by only a few minerals, e.g., pyrite, chalcopyrite,and sphalerite. The success rate jumped to 98% correctly classed deposits when just two rock types were added. Such a high success rate of the probabilistic neural network suggests that not only should this preliminary test be expanded to include other deposit types, but that other deposit features should be added.

Nonrenewable Resources↗