Today, our planet earth is inhabited by ~7.2 billion people, and it is projected to reach ~9 billion by 2050 (UN, 2013).
Most of the earth`s population will be concentrated in developing countries with more than half will be in Africa. Detail report is available from the following url: http://esa.un.org/unpd/wpp/index.htm. The report’s figures are based on a comprehensive review of available demographic data from 233 countries and areas around the world.
With more mouth to feed in the 2050, we need to grow more food for 9 billion people, and we also will meet with the environmental challenge.
The population projections show that feeding a world population of 9.1 billion people in 2050 would require raising overall food production by some 70 percent between 2005/07 and 2050. Production in the developing countries would need to almost double. This implies significant increases in the production of several key commodities. Annual cereal production, for instance, would have to grow by almost one billion tonnes, meat production by over 200 million tonnes to a total of 470 million tonnes in 2050, 72 percent of which in the developing countries, up from the 58 percent (FAO, 2009).
This means that we need more food to feed the 9 billion people by 2050. However, how could we overcome the food limitation through utilized the remote sensing? How we could act appropriately through the technology of remote sensing? And how we could contribute to overcome the issues?
People and food are two variables that connected each other. When we think about food we never thought that our foods also contribute to the global warming through the greenhouse gas (GHG) emission. Agricultural fields and cattle farm are among the greatest contributor to GHG emission. Tropical rain forest conversion and mangroves forest occupation are happening currently to grow crops and raise livestock.
By 2050 we need more land to grow crops and raise livestock, because we need to produce food for ~9 billion people on the earth. Biodiversity is also under threat if we continue to expand the agricultural field in the same way as today. We can no longer grow crops and raise livestock through forest conversion. We need more sustainable ways, stop the expansion and focus to degraded land and high-tech farming management.
What is our opportunity to handle the problems? Remote sensing could help us to answer the questions above as well as to overcome the global warming issue.
Remote sensing is widely used to monitor, assess, and evaluate the planet earth. We have so many opportunities to tackle the mismanagement, global warming, and as well as the issue of food to feed 9 billion people. The following studies are some of the most recent research employed remotely-sensed imagery and techniques that can be utilized to “feed” 9 billion people by 2050.
Agricultural expansion become the main threat for human and environment, only if they are expanding through the sustainable way it will give the benefit to the people and planet earth. One of the main issues of agricultural expansion is oil palm plantations. This type of agriculture converted huge area of tropical rain forests in the tropics. The study by Ramdani and Hino (2013) revealed that the expansion of oil palm plantations was boomed in the period 2000-2010 and occupied not only the tropical forest but also peatlands (Koh et al., 2011). Ramdani & Hino (2013) and Koh et al (2011) employed many scenes of remotely-sensed data to monitor the expansion phenomenon. This method could benefit the farmer only if they have access to the Geo-spatial information, and helped by the expert to understand the situations. Thus, they will understand how to manage the plantation in more sustainable ways.
Crude palm oil (CPO) widely used in the food industry to produce biscuits, crackers, noodles, or even yogurts. In the future, associated with the population growth, the demand of CPO will also be increased. Remote sensing could employ to monitor the oil palm plantation expansion to prevent from the jeopardize disasters (social conflicts, environmental problems, etc.) and provides sustainable solutions.
UAV (Unmanned Aerial Vehicle –red) drones are becoming more important in smaller scale of agricultural fields. Monitor and mapping the fields using the traditional airplanes or satellite are inappropriate since they are costly and have lower spatial and temporal resolution. UAV is the suitable tools for this reason; cheaper and higher spatial and temporal resolution. Torres-Sánchez et al (2014) employed UAV equipped with a commercial camera (visible spectrum) that used for ultra-high resolution image acquisition over a wheat field in the early-season period. Their study used six visible spectral indices (CIVE, ExG, ExGR, Woebbecke Index, NGRDI, VEG) and two combinations of these indices were calculated and evaluated for vegetation fraction mapping. These indices were also spatially and temporally consistent, allowing accurate vegetation mapping over the entire wheat field at any date. This study provides evidence that visible spectral indices derived from images acquired using a low-cost camera onboard a UAV flying at low altitudes are a suitable tool to use to discriminate vegetation in wheat fields in the early season.
This study opens the “door” for the utilization of this technology in precision agriculture applications such as early site specific weed management in which accurate vegetation fraction mapping.
Zarco-Tejada, González-Dugo, & Berni (2012) employed UAV platform to detect the water stress using a micro-hyperspectral imager and a thermal camera. Furthermore, Link, Senner, & Claupein (2013) developed and evaluated an aerial sensor platform (ASP) to collect multispectral data for deriving management decisions in precision farming.
Two studies mentioned above open even wider “door” which can be used for deriving management decisions in terms of precision and efficient management farming. However, policy for the utilization of UAV for public use is still under debate stages, we need more support from the decision maker for a better farming practice to feed 9 billion people by 2050.
Study by Reeves and Bagget (2014) produced the vegetation productivity that was estimated using maximum Normalized Difference Vegetation Index (NDVI) of MODIS satellite platform. Their study revealed that the degradation associated with the historical events that may have had greater impact on vegetation production than present land management practices. Overgrazing and unsustainable fuel wood uses which are usually associated with depressed socio-economic factors. These conditions result in widespread degradation across large areas which are ideal for degradation assessments from coarse scale remote sensing techniques such as the rainfall use as supported variables.
This method also will be useful for yield predictions. The farmer would receive more benefit through the sustainable monitor of vegetation productivity. They will be able to optimize the time to produce more of the agricultural commodities.
Zhou et al. 2014 study found that drought reveals consistent patterns of reduced vegetation greenness in the Congo Basin. It is consistent with decreases in rainfall, terrestrial water storage, water content in above ground woody and leaf biomass. This study uses Enhanced Vegetation Index (EVI) data derived from a satellite-borne sensor, Moderate resolution Imaging Spectroradiometer (MODIS). This study could help the other region in Africa and other part of the world to continuously monitor and assess their environment and find the answer how to face the drought phenomenon.
However, as the author mentioned in the article, the effects of long-term drought on vegetation are more complex than short-term drought and different satellite products measure different properties of vegetation and moisture. Its means we need to work together and combine many type of satellite sensor to overcome the long-term drought on agricultural commodities.
Wittenberg et al. 2014 make the case that these multi-decadal epochs of enhanced and subdued ENSO (El-Nino Southern Oscillation –red) nactivity. This study produce reasonably realistic, decadally predictable high-latitude climate signals, as well as tropical and extratropical decadal signals that interact with ENSO.
This study has proven that we will have more and more accurate weather forecast, where and when the El Niño will develop and how strong it may become. So the farmers could prepare to face the changing climate and save the agriculture commodities from the fail.
Rapid growth is expected to continue over the next few decades in countries with high levels of fertility such as Nigeria, Niger, the Democratic Republic of the Congo, Ethiopia and Uganda, Afghanistan and Timor-Leste, where there are more than five children per woman (UN, 2013). India is expected to become the world’s largest country, passing China around 2028, when both countries will have populations of 1.45 billion. After that, India’s population will continue to grow and China’s is expected to start decreasing. Meanwhile, Nigeria’s population is expected to surpass that of the United States before 2050 (UN, 2013).
From this information, it is an obvious reason that we need to act and collaborate to provide the Geo-spatial information accessible free for the people in the developing countries. All of the research mentioned above will be useless to a human being if only specific people could access and understand the results. Collaboration needs to be involved the lower level of education of farmers and use easier language to be understandable by them.
Knowledge and technological transfer through;
The decision maker and scientists need to sit and discuss together to overcome the issues and give the best to their people. Furthermore, the policy has to be compiled with the scientific findings and implemented in an appropriate way to support the farmers.
FAO (2009) High Level Expert Forum – How to Feed the World in 2050. Available at http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf
Koh LP, Miettinen J, Liew SC, Ghazoul J (2011) Remotely sensed evidence of tropical peatland conversion to oil palm. Proceedings of the National Academy of Sciences 108: 5127–5132. DOI: 10.1073/pnas.1018776108
Link, J., Senner, D., & Claupein, W. (2013). Developing and evaluating an aerial sensor platform (ASP) to collect multispectral data for deriving management decisions in precision farming. Computers and Electronics in Agriculture, 94, 20–28. doi:10.1016/j.compag.2013.03.003
Ramdani, F., & Hino, M. (2013). Land Use Changes and GHG Emissions from Tropical Forest Conversion by Oil Palm Plantations in Riau Province, Indonesia. PLoS ONE, 8(7) DOI: 10.1371/journal.pone.0070323
Reeves, M.C and Bagget, L.S (2014) A remote sensing protocol for identifying rangelands with degraded productive capacity. Ecological Indicators, 43, 172-182. doi: 10.1016/j.ecolind.2014.02.009
Torres-Sánchez, J., Peña, J. M., de Castro, A. I., & López-Granados, F. (2014). Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture, 103, 104–113. doi:10.1016/j.compag.2014.02.009
United Nations (2013) World Population Prospects: The 2012 Revision. Available at http://esa.un.org/unpd/wpp/index.htm
Wittenberg, A. T., Rosati, A., Delworth, T. L., Vecchi, G. A. & Zeng, F (2014) ENSO Modulation: Is It Decadally Predictable?. Journal of Climate, 27, 2667-2681. doi: http://dx.doi.org/10.1175/JCLI-D-13-00577.1
Zarco-Tejada, P. J., González-Dugo, V., & Berni, J. A. J. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment, 117, 322–337. doi:10.1016/j.rse.2011.10.007
Zhou et al (2014) Widespread decline of Congo rainforest greenness in the past decade. Nature, 509, 86-90. doi:10.1038/nature13265