Improving the Yield and Revenue of Indian Crop Production Using Data Engineering

2021 ◽  
pp. 197-208
Author(s):  
Jayashree Domala ◽  
Manmohan Dogra ◽  
Kevin Dsouza ◽  
Dwayne Fernandes ◽  
Anuradha Srinivasaraghavan
Author(s):  
Sujata Mulik

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 


Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 458
Author(s):  
Tara A. Ippolito ◽  
Jeffrey E. Herrick ◽  
Ekwe L. Dossa ◽  
Maman Garba ◽  
Mamadou Ouattara ◽  
...  

Smallholder agriculture is a major source of income and food for developing nations. With more frequent drought and increasing scarcity of arable land, more accurate land-use planning tools are needed to allocate land resources to support regional agricultural activity. To address this need, we created Land Capability Classification (LCC) system maps using data from two digital soil maps, which were compared with measurements from 1305 field sites in the Dosso region of Niger. Based on these, we developed 250 m gridded maps of LCC values across the region. Across the region, land is severely limited for agricultural use because of low available water-holding capacity (AWC) that limits dry season agricultural potential, especially without irrigation, and requires more frequent irrigation where supplemental water is available. If the AWC limitation is removed in the LCC algorithm (i.e., simulating the use of sufficient irrigation or a much higher and more evenly distributed rainfall), the dominant limitations become less severe and more spatially varied. Finally, we used additional soil fertility data from the field samples to illustrate the value of collecting contemporary data for dynamic soil properties that are critical for crop production, including soil organic carbon, phosphorus and nitrogen.


COVID-19 has become a pandemic affecting the most of countries in the world. One of the most difficult decisions doctors face during the Covid-19 epidemic is determining which patients will stay in hospital, and which are safe to recover at home. In the face of overcrowded hospital capacity and an entirely new disease with little data-based evidence for diagnosis and treatment, the old rules for determining which patients should be admitted have proven ineffective. But machine learning can help make the right decision early, save lives and lower healthcare costs. So, there is therefore an urgent and imperative need to collect data describing clinical presentations, risks, epidemiology and outcomes. On the other side, artificial intelligence(AI) and machine learning(ML) are considered a strong firewall against outbreaks of diseases and epidemics due to its ability to quickly detect, examine and diagnose these diseases and epidemics.AI is being used as a tool to support the fight against the epidemic that swept the entire world since the beginning of 2020.. This paper presents the potential for using data engineering, ML and AI to confront the Coronavirus, predict the evolution of disease outbreaks, and conduct research in order to develop a vaccine or effective treatment that protects humanity from these deadly diseases.


2021 ◽  
pp. 2150012
Author(s):  
Isaac Dasmani ◽  
Samuel K. N. Dadzie

In most developing countries, climate variabilities and discount rate played an integral role in the decision-making of farmers, which mostly affect their net revenue. Our study employs Ricardian models to empirically verify this hypothesis using data collected from three major agro-climatic zones in Ghana. We particularly estimated the comparative effect of climate change variability, discount rate, and soil fertility; due to trade-off effect of certain farm practices in response to climate change across major climatic zones and also the fact that discount rate becomes an extremely critical issue in formulating and evaluating conservation and management policy to address climate change. The result indicates that discount rate has a positive and significant effect on the farmers’ net revenue. Further, effect of changes in temperature on food crop production and hence net revenue is more felt in the forest and savannah zones. On the other hand, an increase in rainfall has significant negative effects on crop net revenues and whole-farm net revenue, but a positive effect on net revenue of farmers in the savannah zone. We also found a significant increase in soil fertility to increase crop net revenues.


2020 ◽  
Vol 12 (20) ◽  
pp. 8723
Author(s):  
Justin Dijak ◽  
Laura McCann ◽  
Caroline Brock

Horse operations may produce high amounts of manure per acre/ha and be less aware of recommended manure management practices than livestock farmers, leading to negative environmental impacts. This study compared the manure management practices of two populations of horse owners in the USA state of Missouri, commercial horse operations and an Old-Order Amish community, using data from a 2019 mail survey with a 50% response rate. In commercial operations, manure was more likely to be piled rather than spread directly on fields, which was the Amish practice. The Amish were more likely to use manure for crop production, to indicate that was why they had not explored markets for manure, and to test soil for nutrients. Regression results for factors affecting previous sales/transfers of manure or compost showed that selling was more likely for commercial operations, female operators, and those who had composted manure. Compared to respondents who agreed that manure management had an impact on water quality, those who did not know or were neutral about that statement were more likely to have sold manure. While both groups can improve manure management and are underserved by traditional agricultural information channels, educational efforts should be tailored to their different circumstances.


2011 ◽  
Vol 40 (3) ◽  
pp. 471-487 ◽  
Author(s):  
Heather Klemick

This study examines the drivers of land use in a shifting cultivation system with forest fallow. Forest fallow provides on-farm soil quality benefits, local hydrological regulation, and global public goods. An optimal control model demonstrates that farmers have an incentive to fallow less than is socially optimal, though market failures limiting crop production can have a countervailing effect by encouraging fallow. An econometric model estimated using data from the Brazilian Amazon suggests that fallowing does not result from internalization of local fallow services but instead is associated with poor market access and labor and liquidity constraints.


Author(s):  
Israel Lorenzo-Felipe ◽  
Carlos A Blanco ◽  
Miguel Corona

Abstract Bees and some wasp species of the superfamily Apoidea pollinate most of the crops used for food and feed, producing different impacts on agricultural production. Despite the considerable importance of Apoidea, the relevance of this group’s impact on global crop production and human diets is controversial. To measure the pollination effect of these insects on crop production, factors such as the myriad of agricultural practices, different crop varieties, fluctuating pollinators’ densities, constantly changing environmental conditions, and demands for food items in a diverse diets must be considered. An ‘Apoidea impact factor’ (AIF), a value calculated taking into consideration the effect of this superfamily on enhancing crop production through pollination, the diversity of crops in a given area, the area planted by specific crops, and agricultural output, was calculated for 176 agricultural crops. Consistently with previous estimations, our results show that Apoidea have a direct impact on 66% of the 128 most important agricultural crops consumed in the world. However, the analysis of the impact of Apoidea on global production and human consumption revealed a different perspective: Apoidea pollination affects only 16% of the total tonnage output, 14% of the cultivated area, and 9% of the kilocalories consumed. Because 25 of the most cultivated crops in the world do not require, or are slightly affected by Apoidea pollination, and these plants grow in 84% of the world’s cropland, constituting 50% of the world’s diet, and 89% of the kilocalories consumed by peoples around of the world, the AIF at the world level is reduced to 11% of food consumed, and 6% of the kilocalories. The AIF, when applied to a small geographical scale, for example, the municipality or county level rather than country or state level, becomes more useful identifying areas where bees and wasps have greater impact in agriculture. In this report, we update the widely popular quote ‘One out of every three bites of food we eat is a result of pollinators like honey bees’ to a more accurate one: ‘nearly 5% of the food we eat, and about 10% of the calories we burn have a direct relationship with Apoidea pollination’. This new estimate does not diminish the need for pollinators for many of the world’s most nutritious foods, but merely suggests that these foods do not provide an extensive part of the human diet. The AIF can be used to identify specific areas where these pollinators have greater impact and direct conservation efforts directly into them. This approach can serve as a better estimate of the role of these pollinators in our food, using data-driven arguments.


2008 ◽  
Vol 35 (10) ◽  
pp. 925 ◽  
Author(s):  
Youhong Song ◽  
Colin Birch ◽  
Jim Hanan

Substantial progress in modelling crop architecture has been made under optimal watering conditions; however, crop production is often exposed to water stress. In this research, we develop methods for implementing the simulation of maize (Zea mays L.) canopy architectural development under water stress using data from a maize field trial in 2006–07. Data of leaf number, leaf and internode extension were collected using non-destructive and destructive sampling at 2–3 day intervals. Water stress reduced the extension rate of organs and, therefore, their final length, the reduction being greater as severity of water stress increased. The duration of extension of organs in most phytomers was not significantly affected by water stress. Also, the rate of extension during the linear phase responded linearly to fraction of extractable soil water. An existing 3-D architectural model ADEL-Maize was revised using relationships developed in this study to better incorporate effects of water stress on organ extension and production. Simulated canopy production under three water regimes was validated by comparing predicted final leaf and internode length, plant height and leaf area to independent observations. The analysis and simulation showed that maize organ extension and final length under water stress can be adequately represented by simple linear patterns that are easily integrated into models.


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