Month: August 2021

In Agriculture,

Machine learning (ML) is a trending technology currently that can be used in the modern agriculture industry. ML is a scientific field that enables machines to learn in absence of a strict programme. ML has come up with technologies that have big data and are of high performance creating opportunities for data science ¹.

Agriculture plays a key role in the global economy hence pressure to sustain the agricultural economy with a growing population. Digital agriculture, formally known as precision farming and agri-technology have emerged. This is due to new scientific fields that use data intensive approaches to manage agricultural productivity while consequently mitigating its impacts on the environment. Precision agriculture (PA) is an application of principles and technologies using information to drive temporal and spatial variability to increase effectiveness while minimizing environmental degradation ².

In modern agriculture, the data generated for its operations are provided by different sensors which facilitates a better understanding of an operational environment such as an interaction of advanced soil, crop and weather conditions ¹.

Considering a dimension of precision agriculture concentrating on plant-driven management of crop, delivering treatments such as fertilizer application, irrigation and pesticides use is possible. This is facilitated by monitoring of crop, soil, and climate in a field by incorporating decision support systems such as sensors that are able to learn ².

Increasing farm production on an increasingly smaller land and reducing consumption of resources such as fertilizers and water influence establishment of new techniques.

Digitization and technological development are the future and a promising approach in the same. With further research and development, a more precise approach in agricultural predictions will ensure timely approaches in responding to issues related to climate risks and those threatening food security at a farm level as well as global scale.

1            Liakos, K. G., Busato, P., Moshou, D., Pearson, S. & Bochtis, D. Machine learning in agriculture: A review. Sensors 18, 2674 (2018).

2            Dimitriadis, S. & Goumopoulos, C. Applying Machine Learning to Extract New Knowledge in Precision Agriculture Applications.

One of the major global challenges with the growing population is to ensure food security for all at the same time ensuring sustainable development. Postharvest losses (PHL) are a threat to sustainability of agricultural food systems and livelihoods of small-scale farming communities.

PHL occurs in the field, during transit to the markets and holding facilities as well as in storage ⁵. My thesis paper focused on storage postharvest losses in a household level with an interest in pests and pathogen losses. This is because according to FAOSTAT, losses that occurred in storage in smallholder farms were reported to be greater than losses caused in the transport, wholesale and harvesting levels. Additionally, poor storage structures in developing countries are the main causes of postharvest food loss in the supply chain ⁶.

The key focus of the study was on storage technologies which have been proposed to reduce PHL in Evidence of Resilience Agriculture (ERA) dataset. ERA is an analytical engine in World Agroforestry (ICRAF). The technologies include improved physical storage such as use of improved granaries or use of hermetic bags to reduce pest and pathogen losses and improved chemical storage where preservation of grains and fruits was done using diatomaceous earth (DE) or other chemicals. Use of hermetic containers and DEs as well as fumigants or other preservation chemicals helps in preventing insects and moulds ⁷.

An analysis was done to determine by what percent the proposed technologies reduced postharvest losses in storage facilities compared to control treatments. This was run using an ERA package developed for use in R Studio. In combination, the improved physical and improved chemical storage performed better than them being implemented individually. Improved physical storage reduced postharvest storage losses at a higher level compared to improved physical storage. This is because pests and pathogen may become resistant to agro-chemicals used in the storage facilities to minimize pests and pathogen postharvest losses such as fumigants and pesticides ⁸. Moreover, combination of different technologies such as use of improved varieties of seeds and sofa grain for chemical treatment proved more effective too.

At a household level, a farmer can reduce the storage losses incurred by adopting the proposed technologies to reduce the pests and pathogens postharvest losses. This increases the amount of food available for household consumption for a household that relies on agricultural production for household level consumption and more for those who sell part of their produce.

             REFERENCES

1            Papargyropoulou, E., Lozano, R., Steinberger, J. K., Wright, N. & bin Ujang, Z. The food waste hierarchy as a framework for the management of food surplus and food waste. Journal of cleaner production 76, 106-115 (2014).

2            Aulakh, J., Regmi, A., Fulton, J. R. & Alexander, C. E. Estimating post-harvest food losses: Developing a consistent global estimation framework.  (2013).

3            Costa, S. J. Reducing Food Losses in Sub-Saharan Africa. An ‘Action Research’Evaluation Trial from Uganda and Burkina Faso (2014).

4            Benhalima, H., Chaudhry, M., Mills, K. & Price, N. Phosphine resistance in stored-product insects collected from various grain storage facilities in Morocco. Journal of Stored Products Research 40, 241-249 (2004).

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Land equivalent ratio (LER) in agriculture refers to relative land required in area under monoculture or sole cropping to give the same yields as under polyculture or intercropping. The LER ratio refers to the ratio between comparative yield of each crop and tree species in agroforestry compared to the yield in a monoculture system of the same crop and tree species over same period of time. Can be used where a producer can obtain more than one yield in the same area ⁹.

 Sustainable intensification of agriculture is required to meet future increasing food demand while reducing the agro-ecological footprint ¹⁰. Diversification of agricultural production systems enhances agro-ecological strategies like increasing biodiversity and higher crop protection.

Intercropping as a strategy increases agricultural productivity per unit of land by improving resource capture ¹⁰. Intercropping can be a guide to diversify agro-ecological systems by using leguminous crops and reducing on the use of mineral fertilizers ¹¹.

In agroforestry, the LER serves as a productivity indicator by evaluating yields from crops and trees together in comparison to those from a monoculture cropping system ⁹. The integration of arable crops and trees have justified benefits such as improved soil fertility, protection against erosion and biodiversity ¹². An LER greater than 1 indicates that production is higher in the agroforestry system compared to that in a monoculture system ⁹.

              REFERENCES

1            Seserman, D.-M. in European Agroforestry Conference-Agroforestry as Sustainable Land Use, 4th.   (EURAF).

2            Yu, Y., Stomph, T.-J., Makowski, D. & van der Werf, W. Temporal niche differentiation increases the land equivalent ratio of annual intercrops: a meta-analysis. Field Crops Research 184, 133-144 (2015).

3            Salehi, A., Fallah, S., Neugschwandtner, R. W., Mehdi, B. & Kaul, H.-P. Growth analysis and land equivalent ratio of fenugreek-buckwheat intercrops at different fertilizer types.

4            Jose, S. Agroforestry for conserving and enhancing biodiversity. Agroforestry Systems 85, 1-8 (2012).

 Agricultural commodities go through several operations such as transportation of outputs, processing, and storage before getting to the consumer. Outputs undergo significant losses during these handling stages. The sum total of quantity lost in the process is known as postharvest losses (PHL) ¹. In developing counties, the importance of PHL has not been fully identified in instances where agricultural production has not been linked to marketing¹.  Major causes of postharvest losses at harvest are insect pests and diseases, harvesting method and time, crop variety not resistant to pests and pathogens, and unfavourable weather conditions.

Investments in production resources such as fertilizers and irrigation are higher than those focused on postharvest-related losses which result into failure to meet food security. On the contrary, reduction of postharvest losses increases food available for consumption. Information on losses and the extent at different levels of the value chain is not only technologists and scientists but also useful for industrialists, administrators and policy makers ¹.

In a world where farmers face struggles in rural developing economy and resources being scarce, reducing PHL allows them to have more crops and increased supply of grains ². Quality losses may lead to economic losses when a farmer’s produce losses attributes which make it less attractive to consumers. Economic losses occur when a product misses on a market opportunity ³.

Fruits and vegetables are more susceptible to quantity and quality deterioration in tropic conditions especially due to their moisture content ¹. It not only reduces availability but also leads to increase in unit cost of marketing and transport ⁴. This affects producers in shares of commodity price as well as consumers where prices are increased due to reduced availability.

While the goal of a policy is well comprehended, the micro-economics associated with loss are not ². However, in absence of objective and reliable estimates, on PHL at different stages of the value chain, from production to consumption, establishment of policies on reduction becomes a challenge.

REFERENCES

1            Kumar, D. K., Basavaraja, H. & Mahajanshetti, S. An economic analysis of post-harvest losses in vegetables in Karnataka. Indian Journal of Agricultural Economics 61 (2006).

2            Goldsmith, P. D., Martins, A. G. & de Moura, A. D. The economics of post-harvest loss: a case study of the new large soybean-maize producers in tropical Brazil. Food Security 7, 875-888 (2015).

3            Affognon, H., Mutungi, C., Sanginga, P. & Borgemeister, C. Unpacking postharvest losses in sub-Saharan Africa: a meta-analysis. World Development 66, 49-68 (2015).

4            Murthy, D. S., Gajanana, T., Sudha, M. & Dakshinamoorthy, V. Marketing and post-harvest losses in fruits: its implications on availability and economy. Indian Journal of Agricultural Economics 64 (2009).