As the population continues to grow, it is essential to increase the production yields of our crops. Agriculture and farming industries are often viewed as green economic activity, but every human action still has an impact on nature. Worldwide historical carbon (C) losses due to Land Use and Land-Use Change between 1870 and 2014 are estimated at 148 Pg C (1 Pg = 1 billion ton) (João Carlos de Moraes Sá, 2017).
Soil holds around double the amount of carbon than is found in the atmosphere and vegetation worldwide. Nevertheless, unsustainable land management practices cause a progressive loss of carbon. This represents a global threat as when carbon is ‘unlocked’ it can enter the atmosphere and contribute to climate change. Soil carbon loss also makes soils less fertile and therefore impacts global food security.
It is important to approach the challenge from a wider perspective that considers the environmental impact of intensification strategies. Sustainable intensification (SI) involves increasing productivity without damaging the environment, this carried out through the use of emerging Smart technologies while trying to create social and environmental benefits. On the other hand, eco-efficiency is a compromise between economic and ecological performance
Different strategies have been studied in order to help decrease agricultures carbon footprint and to isolate carbon. Many of these strategies bring in new technologies such as data intelligence and automation in order to obtain greater results adapted to each case study.
Restoration of degraded pasturelands (Fernandes, 2006). Low levels of available soil nutrients (phosphorus) and presence of toxic compounds (aluminium) are common struggles present in pasturelands. Other issues caused by pasture in soil are low vegetation biomass, depleted seed banks, high seed predation, and low stump sprouting (Nepstad et al., 1990), as well as soil surface sealing and compaction (Eden et al., 1991) This is caused by a poor management of livestock and pastures (within 7-10 years). The restoration strategy should pursue the recovery of the whole damaged ecosystem to reestablish the preexisting biotic integrity in terms of species’ composition and community structure. Some companies such as BioCarbon Engineering®, provide an integrated approach to scalable ecosystem restoration, combining automation with data driven intelligence.
Planted commercial forest and forestation. This approach is defined as the human-induced conversion of nonforested land to forested land through planting. For example, Terviva® is an AgTech start-up which grows a hardy orchard crop called pongamia. This is a non-GMO tree crop that can be grown with little or no irrigation, and produces oilseeds that are processed into oil for biofuel, plant protein for animal feed or biogas, and biomass for baseload electricity generation. Symbiosis investimentos® and other companies have produced native species to maximise biological growth and adaptability to media (Pyrch, 2018).
Integrated crop-livestock-forestry systems (Alves, 2017) positive synergistic effects on physical, chemical and biological soil properties help decrease degradation while improving agricultural yield. Agroforestry trends combine trees in the integration and silvopastoral trends include trees in pastures. The benefits include: soil C accumulation and mitigation of nitrous oxide (N2O) emissions. Assmann et al. (2017) demonstrated for a soybean (Glycinemax)-pasture (black oats +Lolium multiﬂorum) summer–winter sequence that the grazing intensity affects the release rates of P and K through animal excreta and plant residues; which has implications for fertilisation timing.
No-till cropping systems. This technique improves the soils biological fertility, resilience, retention of organic matter and nutrient availability. If performed correctly it can reduce labour, fuel, irrigation and machinery costs. The water retention can increase yield and erosion is reduced. This approach is also very beneficial to biota and wildlife since layers remain in their natural state. To reduce weeds, pests and disease, crop rotation or cover crops can be used.
Another approach for the reduction of the carbon footprint of agrofarming activities is the recycling of organic wastes such as sludge for the production of biomethane and compost or biofertilizers.
Hydroponic crops are also a great approach to maximize production yields in areas with low soil availability and scarcity of water. Crops are planted without any soil and are constantly fed with a nutrient solution. Crop yields can be up to 100 times higher than conventionally grown plants. However, the structural costs are naturally higher so the initial investment can be a handicap.
The inclusion of ICT emerging technologies for precision agriculture: Internet of Things and Cloud Computing are expected to be used to introduce more robots and artificial intelligence into farming practices (Wolfert, 2017).
Currently, some technologies are already used to optimize fertilizer, pesticide and herbicide application and optimal planting dates calculation. For example, Unmanned Aerial Vehicle-(UAV) (Rokhmana, 2015) based platforms have been tested to support precision agriculture mapping. Some of the information for land preparation, cadastre boundary, vegetation monitoring, plant health, and stock valuation can be checked periodically with these devices easily and in a cost-effective way. The system uses data processing with digital photogrammetric technologies to produce imagery with spatial resolution <10 cm, measuring parcel area, assessing the individual trees or plants stock, and topography. More than 500Ha can be analysed a day. The Orthophoto image can provide visual interpretations such as the individual trees structure, plant density, and parcel boundary area, while Digital Elevation Model (DEM) developed could assess tree’s height information and terrain topography with accuracy 3-6 pixel or 0.5-2.5 m.
The future of Smart Farming could take two different paths:
1) the optimization of the food supply chain in terms of planning, monitoring, control, optimization and documentation of where the farmer sits within that chain,
2) open collaborative systems in which the farmer and other stakeholders can choose business partners easily. The first path would enhance current practices while the second would have a deeper effect in the business model of the food supply chain.
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- João Carlos de Moraes Sá, R. L. (2017). Low-carbon agriculture in South America to mitigate global climate change and advance food security. Environment International, Volume 98, Pages 102-112.
- Mulongoy, K. (1992). Technical paper 2: Biological nitrogen fixation. En P. J. Bansh R. Tripathl, The AFNETA alley farming training manual – Volume 2: Source book for alley farming research (pág. Technical paper 2: Biological nitrogen fixation). Alley Farming Research Network for Africa. Obtenido de http://www.fao.org/wairdocs/ilri/x5546e/x5546e05.htm
- Pyrch, J. (31 de January de 2018). Sustainable Brands. Obtenido de sustainablebrands.com
- Rokhmana, C. A. (2015). The Potential of UAV-based Remote Sensing for Supporting Precision Agriculture in Indonesia. Procedia Environmental Sciences, Volume 24, 245-253.
- Wolfert, S. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, Pages 69-80.
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