Crop production and its determinants.
\\n\\n
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\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/132"}},components:[{type:"htmlEditorComponent",content:'With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"177",leadTitle:null,fullTitle:"Nuclear Power - Deployment, Operation and Sustainability",title:"Nuclear Power",subtitle:"Deployment, Operation and Sustainability",reviewType:"peer-reviewed",abstract:"We are fortunate to live in incredibly exciting and incredibly challenging time. Energy demands due to economic growth and increasing population must be satisfied in a sustainable manner assuring inherent safety, efficiency and no or minimized environmental impact. These considerations are among the reasons that lead to serious interest in deploying nuclear power as a sustainable energy source. At the same time, catastrophic earthquake and tsunami events in Japan resulted in the nuclear accident that forced us to rethink our approach to nuclear safety, design requirements and facilitated growing interests in advanced nuclear energy systems. This book is one in a series of books on nuclear power published by InTech. It consists of six major sections housing twenty chapters on topics from the key subject areas pertinent to successful development, deployment and operation of nuclear power systems worldwide. 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Adhesive bonding is a recent technology that in many applications, can replace the techniques already known in engineering, such as rivets, bolts, welds, etc. The adhesives have the advantage of being lightweight. The use of adhesives in engineering is already present in several areas, for example, aeronautics, nautical, renewable energy, mechanics, etc.
\r\n\tAs the subject of adhesives is in constant development, this book's purpose is to get together information about adhesives science and technology, recent advances, and applications that use adhesive technology. Also, to make these contents available to engineering students, engineers, researchers, and the people interested in this topic. The book is expected to present works that aim to contribute to the development of new technologies and the use of non-traditional materials in engineering.
In the 1950s, it was discovered in the biomedicine field that hydrogen atoms in water molecules can produce nuclear magnetic resonance phenomena [1]. Nuclear magnetic resonance was used to obtain information from the distribution of water molecules in the human body, by which the internal anatomy of the human body could be mapped accurately [2]. After decades of development, MRI has become indispensable medical imaging devices [3]. The influence of MRI on the clinical and life science comes from its unrivaled imaging capabilities, and it can obtain not only clear structural images of the anatomic structure and the organic lesion completely without trauma [4], but also the other physiological information.
\nIn recent years, MRI techniques have developed rapidly, especially toward high-field imaging, such as 7 T, 9.4 T or even higher field strengths [5]. MRI technique development requires an associated performance improvement in the system hardware, which mainly includes the main magnet [6], gradient coil [7], and radio-frequency (RF) coil [8].
\nEarly MRI magnet system mainly used ferromagnetic shield structure [9]. The use of large amounts of ferromagnetic shield [10] makes the weight and size of the system relatively large and installation costs of the system high. With the rapid development of magnet technologies, the active shield structure has been successfully developed for the high-field magnet system [11], which greatly reduces the scope of 5 Gauss line. Generally, the superconducting magnet consists of multiple solenoidal coils and shielded coils [12]. The inner solenoid coil is called the primary coil, generally through forward current. The outer solenoid coil is called shield coils, through the reverse current. Open MRI system helps improve patient comfort and expands the scope for the patient [13]. It is easy to achieve a high magnetic field by using combination of iron core and superconducting coil. The cryogenic system is used to keep superconducting wire in a cryogenic environment and ensure safe operation of the superconducting magnet.
\nIn the Institute of Electrical Engineering, Chinese Academy of Sciences, (IEE, CAS), several sets of MRI magnets have been designed or fabricated, including 0.7 T planar whole-body MRI system, 1.5 T cylindrical whole-body MRI system, 7.0 T animal MRI system, and 9.4 T cylindrical whole-body MRI system [14].
\nDue to structural advantages of the open MRI system, it can be applied to interventional therapy. The shape of magnetic field depends mainly on yoke and pole, and coils provide magnetic source. The magnet system has less superconducting wire, and only 120 L of liquid helium with zero boiling off liquid helium by one GM cryocooler. Magnetic field strength of superconducting open MRI is generally higher than 0.7 T due to a higher uniform magnetic field produced by superconducting coils [15]. \nFigure 1\n shows a 0.7 T open MRI system designed by IEE, CAS.
\nA 0.7 T whole-body open MRI system.
A 9.4 T whole body imaging system is developed in IEE, CAS, shown in \nFigure 2\n. The magnet has a horizontal length of 3.5 m and large warm bore with a diameter of 0.8 m. The magnet system is designed with the minimum cost of wire consumption. By means of the optimal algorithm, the coil sizes and positions are optimized to reduce the coil volume and constrain the magnetic field inhomogeneity. In the optimization, the coil stress and current margins are also constrained to satisfy the coil safety requirements. Fully stable NbTi superconducting wire type WIC (Wire-In-Channel) is employed to wind the coils. Both active and passive quench protection system are employed to protect the magnet from damage during a quench event. The magnet system will reach the final field homogeneity as low as 0.1 ppm (peak to peak) in the central 30 cm DSV and the stability of 0.05 ppm/h. The magnet is cooled by liquid helium bath, and the evaporated helium is condensed by two re-condensing cryocoolers.
\nA 9.4 T superconducting magnet for whole-body MRI system.
A gradient coil set is an important component in a standard MRI scanner which produces linear gradient magnetic fields that are superimposed over a strong uniform magnetic field. The uniform magnetic field is produced by a main magnet, which aligns with the proton precession direction. The superimposed gradient magnetic field slightly changes the proton precession frequency or phase, thus encoding the spatial information of an imaged object in the frequency associated with a position in space [16]. In general, the magnetic field gradient produced by the gradient coils is required to be as linear as possible, and a well-designed gradient coil should also have low inductance, low resistance, high efficiency, etc. [17]. This is especially pertinent in high-field imaging and fast imaging when all the coils’ parameters must be highly optimized.
\nIn a gradient assembly, there are three gradient coils, called the x, y, and z coils [18]. \nFigure 3\n shows a set of actively shielded gradient coils (here the actively shielding gradient coil is a coil pattern containing both the primary coil and shielding coil [19]). The red and blue colors of the gradient coils indicate where the current flows in clockwise and anticlockwise [20]. The three-axis gradient coils are fixed by epoxy resin in an encapsulated gradient assembly [21], as is shown in \nFigure 4\n. In an integrated gradient assembly, there are also cooling devices and a shim tray installed [22]. The hard epoxy resin largely impedes the vibration of the gradient coils [23], which avoids torsion and deformation of the gradient coils under strong Lorentz force.
\nActively shielded gradient coils used in an MRI scanner: (a) x gradient coil, (b) y gradient coil, and (c) z gradient coil. The red and blue colors indicate the direction in which the current flows.
Illustration of the three-axis gradient coils fixed in the epoxy resin.
RF coil is the key component of the MRI system, which serves as the transmitter as well as receiver in the formation of the final images [8]. There are various kinds of RF coils. The difference between coils lies in different parts of human body and different field strengths.
\nAccording to imaging part of the human body, it can be classified into head coils, body coils, knee coils or foot coils, etc. No matter how many kinds of coils there may be, all the coils can be basically treated as two kinds of coils, namely surface coil and volume coil. For the surface coils [24], the shape of which is usually a circle, which will facilitate the fabrication of coil. Surface coils are often used as receivers, the reason is that the field it produces is inhomogeneous, which is detrimental to the imaging process. But the signal-to-noise ratio (SNR) of the surface coils is higher than volume coils, partly because it can be located closely to the imaging area. Nowadays, surface coils are not used alone to achieve the receiving purpose. A bunch of surface coils [25], which we call loop array, are used for its good performance in receiving as well as transmitting. An illustration of surface coil is shown in \nFigure 5\n.
\nDiagram of surface coils: (a) single coil and (b) array coil.
Another kind of RF coil is the volume coil, and the most popular volume coil is the birdcage coil [26, 27, 28, 29], which is shown in \nFigure 6\n. A prominent character of birdcage coil is its quadrature excitation strategies both spatially and temporally. The distinctive excitation technology generates a circularly polarized field, which can result in a highly uniform B1 field.
\nDiagram of volume coils: (a) high-pass birdcage volume coil and (b) low-pass birdcage volume coil.
Sketch map of volume coils: (a) birdcage coil and (b) TEM coil.
An alternative volume coil named TEM coil [30] maybe used in ultra-high field MRI as illustrated in \nFigure 7\n. TEM is the most general term for “transmission line,” and it can be realized through many kinds of circuit, such as coaxial lines, strip lines, microstrips or waveguides. In birdcage coils, the end rings form the “return path” of the current on the rungs. But in high frequencies, the end rings can be problematic. The inductance and resonant frequency of birdcage coils are limited by the size of the end rings. And the end rings are related to the diameter of the coil. The magnetic field generated by end rings is along with the B0 field, which is nonproductive for the excitation of nucleus. So, in TEM coils, the end rings are replaced by shield to serve as the return path. The inductance and resonant frequency of TEM coil are independent of the diameters of the coil. A length determined TEM coil can be built to arbitrary diameter, without influence on the frequency. So, its use in the ultra-high field imaging is flexible. The TEM coil can be regarded as a toroidal array of many transmission line elements. The impedance of each element can be modified individually. The reactance between each element can be controlled to achieve parallel imaging applications.
\nFarmers experience various problems in the quest to produce enough crops in order to meet the demand of the ever-teaming population and still keep constant and without comprising the standard of the available resources for generations to come. Mechanisms are needed to enhance soil and plants nutrients in order to increase crop yields, and plant nutrients are one of the requirements to enhance crop production [1, 2].
Plants’ nutritional quality directly affects human nutrition in terms of productivity. It is therefore no gainsaying that the quality of food consumed in a country determines the quality of its populace. In many developing and developed countries, deficiency of micronutrients in pastures and crops has a negative effect on the health of both plants and animals [3]. In addition, the adequate provision of sunlight, air and water is a major prerequisite for optimum plant yield and improved crop management. In a bid to achieve these, various countries have devised means to reduce the negative effects of both abiotic and biotic factors in plants. After dedicating sufficient time and capital for farming, the goal of many Nigerian farmers is to produce sustainable crop yield [1].
However, certain factors come into play which might not augur well for agricultural yield in general and crop production in particular. Considering the rate of food insecurity in most developing countries, resulting from unfavourable weather condition owing to global climate change, the improved sustainable management of plant nutrition has been considered a precondition to reduce the challenge of prevailing hunger in the affected countries, Nigeria not excluded [1]. Owing to inadequate mechanisation and the small-scale nature of agricultural production, Nigeria has not been able to achieve self-sufficiency in food production.
According to Obasi et al. [4], which noted that the sub-Saharan Africa region is among the countries that have continued to experience significant food shortages, more than 40% of the region population is estimated to be suffering from hunger and poor nutrition. Just like many other developing countries, the Food and Agricultural Organisation identified that widespread poverty, poor economic conditions, institutional failure and constraints in logistics, among many other challenges, significantly affect crop production in Nigeria.
In a bid to tackle these challenges, the Nigerian government over the years has intensified efforts towards improving both plant nutrient and crop production mainly through better land use, human resource development in the agricultural sector, research in diversification of types of crops and seeds, fight against pests and diseases and increased use of fertilisers. However, despite the resources devoted to crop production in Nigeria, the productive efficiency of farmers for most crops still fall below 60% [4]. Globally, both socioeconomic and ecological factors interact to determine plant nutrients and sustainable crop production. Efforts to intensify agricultural production in Nigeria has been a continuous process which is taking place through several pathways; therefore, we examined how some of these factors affects plant nutrition and crop production in the case of Nigeria.
Crop production has continued to play a major role in sustaining economic growth in Nigeria. However, its sustainability has been threatened with major challenges overtime. These challenges range from deficiency in plant nutrient as a result of unfavourable biotic and abiotic factors which includes unfavourable climatic conditions, low level of technological development in the agricultural sector, misapplication of fertilisers, infrastructural decay and so on. Various policies have been recommended overtime to address the issue of low crop production in Nigeria. However, it is salient to know how some of these factors have affected plant nutrition and crop production in Nigeria; examined below are some of the factors as identified in the literature.
In an attempt to correct the deficiencies of nutritional elements in crops, a wide range of Nigerian farmers often apply organic and inorganic fertilisers as both play a prominent role in improving soil fertility. However, fertiliser application is a necessary condition for crop yield but not a sufficient condition for an improved crop yield. According to Awodun et al. [5] cited in Ayeni et al. [6], both organic manure and fertilisers play different roles in improving soil fertility, but they both cannot supply all the nutrients in plants that can solely feed a teeming Nigerian population. Nottidge [7] further identified that fertiliser application leads to nutrient imbalance and low infiltration rate, all of which hinders the uptake of nutrients by plants. Also, Ayeni et al. [6] identified that the constant use of inorganic fertilisers can increase the level of soil acidity thereby leading to soil damage.
It has been globally recognised that the most serious threat to agricultural productivity is environmental issue [8]. For countries with higher temperature, the consequences of climate change tend to be more severe. This is most especially true for many developing countries with little adaptive capacity [9]. In recent times, atmospheric emission has been on the increase due to the improper use of agro chemicals, low level of land and environmental management and inadequate manure management. According to Yobannes [9], one of the most important emissions that affects crop productivity and plant nutrient is nitrous oxide, which is determined by fertiliser application, irrigation methods and animal feeds.
Ufiobor [10] further identified that one of the major factors that determines crop yield in Nigeria is the climatic condition. From 1970 to 2018, temperature has increased from an average of 1.4–1.9°C [11] cited in Ufiobor [10]. The northwest, northeast and southwest of the country are especially being affected by extreme harsh weather conditions. The consequence of this is that higher temperature will decrease soil moisture which will have an attendant effect on plant nutrients and crop production.
In the developed economies, most especially Europe and North America, sustainable crop production has been increasing rapidly due to the developed nature of their farming system which has been made possible as a result of innovation and technological enhancement [10]. Farmers in these countries have accepted the evolving change and are now actively engaged in research and training for a sustainable cropping system [10]. In these countries, the government has also implemented programmes to support rigorous scientific investigation that will improve plant nutrition to produce healthy food for its populace. However, Nigeria has not yet witnessed the kind of agricultural evolution that has taken place in developed countries. One major constraint to agricultural development in Nigeria has been the slow response to technological adoption which in turn leads to low productivity and poor farming system, which affects plant nutrients. Nigeria majorly depends on traditional farming system which has an effect on the use of farmlands as the farming system is mainly carried out without the use of machines.
Just like other developing countries, the role of labour force in determining the level of output in all the sectors cannot be undermined. The agricultural system in Nigeria is highly labour intensive as labour force is a crucial part of its production system. According to Ufiobor [10], labour force accounts for over 90% of its total farm operations. Ufiobor [10] further envisaged that this could be as a result of the fact that many of its educated youth have shown little interest in the agricultural sector over the years, thus causing a shortage of skilled labour force in the agricultural sector that can also affect the nutritional value of plants and total crop production itself.
According to the Nations Encyclopedia [12], major crops cultivated in Nigeria include sesame, beans, nuts, cashew, beans, groundnut, cassava, cocoa, gum Arabic, millet, melon, rice, palm kernels, rubber sorghum, banana, plantain, beans and yams. However, the most widely produced crops are cassava and yams in the south and millet and sorghum in the north. Nigerian farmers also grow many fruits and vegetables. In recent years, the use of fertiliser in many countries has been increasing overtime. However, the use of organic wastes for pasture has been more feasible in the developed countries especially China, than in all other countries including Nigeria [13]. This is an indication of the fact that the Nigerian government has not really encouraged the use and development of organic fertiliser in Nigeria which might be responsible for the low level of manure generated for the purpose of farming.
The International Food Policy Research Institute [14] identified that there are signs of an increase use of fertilisers in countries where fertiliser subsidies are being granted to farmers by the government. Prominent among these countries are Malawi, Nigeria and Zambia. The use of fertiliser by Nigerian farmers is however quite common especially among the shareholder farmers. In some cases, these farmers also use some inorganic fertiliser which covers 70% of plots of lands [14]. Since the 1970s, efforts by the Nigerian government to stimulate the demand for fertiliser have been on the increase. This aim has been achieved by growing commercial fertiliser sector through price reduction, extension services to boost soil fertility, increased use of technology and increased access to credit facilities by farmers [15] (Figure 1).
Agricultural fertiliser use. Source: Authors’ computation using excel [
In the year 1980, fertiliser production (kilogrammes per hectare of arable land) in Nigeria was 9,220,000. Over the past 36 years, its highest value was 436,957,273 in the year 2013, while its lowest value was 70,115,000 in the year 2007. The upward and downward movement of this trend is an indication that the level of fertiliser production in Nigeria has not witnessed a stable movement.
According to the International Food Policy Research Institute [14], the types of fertiliser commonly produced and used in Nigeria include urea, nitrogen-phosphorus-potassium (NPK) and superphosphate (SSP). The most commonly used NPK blends are 15-15-15, 20-10-10, 12-12-17 + 2 MgO and 25-10-10. NPK fertilisers are further formulated to be site and crop specific. In a bid to further boost the effective procurement and distribution of fertiliser, the Nigerian government at various times has introduced several measures for its production, procurement and distribution.
In Nigeria, emphasis on increased agricultural productivity of farmers from the perspective of soil conditioning has been on chemical fertiliser, while there has been less emphasis on the impact of the bio-organic input [16]. Even as the quest to ensure the eradication of hunger and poverty has been on the increase, the Nigerian government has taken measures to ensure national self-sufficiency through local fertiliser production, supplemented by importation to ensure adequate and timely fertiliser supply to all farmers. The government also offers a subsidy on the market price of fertiliser so as to make fertiliser affordable to smallholder farmers. Given that the agenda of most successive government is to boost local food production and ensure national self-sufficiency, various efforts has to be intensified to synergise the use of both organic and inorganic medium of improving soil fertility for plant nutrition.
The method used in this study is the survey of literature and stylised facts approach. Relevant data was collected from Food and Agricultural Organization data (FA) data base and the National Bureau of Statistics (NBS) of the Nigerian statistical bulletin for the indicators of crop production and other major agriculture commodities in Nigeria. Tables were used to describe the yields and prices of various agriculture commodities and determinants. Table 1 presents crop production index, employment in agriculture (male, female and total employment in the agricultural sector) and agricultural land.
Year | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Crop production | 80.5 | 79.4 | 83.3 | 87.8 | 93.7 | 99.5 | 106.9 | 97.5 | 104.3 | 90.1 | 104.5 | 96.3 | 107.5 | 104.3 | 117.8 | 120.1 | 118.9 | — |
Total agriculture employment | 57.3 | 57.6 | 60.7 | 58.2 | 51.9 | 51.2 | 49.6 | 48.6 | 44.0 | 40.4 | 30.6 | 33.1 | 35.9 | 38.3 | 36.8 | 36.4 | 36.3 | 36.5 |
Male agriculture employment | 51.1 | 51.7 | 55.8 | 52.7 | 45.3 | 44.4 | 42.7 | 41.7 | 35.8 | 30.9 | 19.4 | 22.0 | 25.3 | 28.2 | 26.7 | 26.3 | 26.1 | 26.2 |
Female agricultural employment | 61.9 | 61.9 | 64.2 | 62.3 | 57.0 | 56.3 | 54.9 | 54.0 | 50.5 | 48.0 | 39.5 | 42.2 | 44.7 | 46.7 | 45.2 | 44.8 | 44.9 | 45.3 |
Agriculture land area | 78.3 | 76.3 | 77.5 | 78.8 | 78.8 | 79.8 | 80.5 | 80.9 | 79.8 | 75.8 | 76.9 | 78.0 | 79.1 | 77.7 | 77.7 | 77.7 |
Crop production and its determinants.
Source: Authors’ compilation.
From Table 1, crop production in Nigeria shows an increase and decrease trend; it was observed among those that are employed in the agriculture; the number of women in agriculture is more than the number of men in agriculture. The price of various agricultural items across Nigeria in 2017 is presented in Table 2.
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Item labels | 42736.0 | 42767.0 | 42795.0 | 42826.0 | 42856.0 | 42887.0 | 42917.0 | 42948.0 | 42979.0 | 43009.0 | 43040.0 | 43070.0 |
Agric eggs (medium size price of one) | 47.4 | 42.9 | 43.9 | 46.2 | 45.7 | 45.3 | 44.3 | 42.9 | 45.7 | 42.1 | 40.8 | 41.3 |
Beans: brown, sold loose | 353.6 | 337.1 | 353.3 | 357.2 | 365.9 | 374.3 | 382.3 | 370.3 | 404.8 | 382.6 | 369.8 | 362.0 |
Beans: white black eye, sold loose | 305.5 | 309.9 | 318.5 | 324.0 | 332.3 | 339.6 | 344.0 | 335.7 | 358.1 | 342.1 | 337.1 | 314.9 |
Beef bone in | 1001.2 | 995.6 | 1010.3 | 1035.4 | 1123.7 | 1129.0 | 1128.9 | 1151.4 | 1078.5 | 1081.3 | 1065.6 | 1067.7 |
Beef, boneless | 1249.5 | 1270.7 | 1281.7 | 1323.1 | 1378.9 | 1393.4 | 1376.9 | 1276.9 | 1324.9 | 1312.7 | 1286.9 | 1236.4 |
Bread sliced 500 g | 302.9 | 299.7 | 297.5 | 296.6 | 307.7 | 320.6 | 314.5 | 304.2 | 310.6 | 305.9 | 299.0 | 290.1 |
Bread unsliced 500 g | 270.3 | 264.9 | 262.3 | 277.7 | 282.3 | 286.8 | 285.6 | 258.7 | 286.3 | 280.3 | 274.3 | 268.2 |
Broken rice (Ofada) | 377.4 | 392.0 | 421.5 | 425.5 | 460.1 | 472.3 | 473.4 | 415.3 | 431.6 | 416.2 | 417.2 | 319.9 |
Catfish (obokun), fresh | 899.6 | 885.1 | 884.1 | 886.7 | 900.7 | 916.5 | 910.6 | 921.2 | 894.5 | 906.9 | 902.0 | 919.8 |
Catfish, dried | 2204.1 | 2150.2 | 2189.6 | 2189.1 | 2213.7 | 2255.2 | 2215.3 | 2176.8 | 2159.0 | 2083.7 | 2214.3 | 2146.6 |
Catfish, smoked | 817.1 | 825.3 | 834.3 | 837.1 | 845.9 | 848.4 | 853.2 | 852.9 | 839.5 | 845.1 | 847.7 | 1007.4 |
Chicken feet | 765.3 | 785.4 | 907.5 | 768.1 | 819.6 | 832.8 | 832.7 | 834.7 | 817.2 | 800.1 | 790.9 | 1156.0 |
Chicken wings | 919.1 | 963.5 | 980.9 | 886.6 | 925.6 | 952.2 | 958.0 | 946.9 | 979.3 | 960.7 | 953.6 | 950.6 |
Dried fish sardine | 959.1 | 935.5 | 958.7 | 972.7 | 991.6 | 989.5 | 968.8 | 972.6 | 947.6 | 967.0 | 968.8 | 1077.8 |
Evaporated tinned milk carnation, 170 g | 136.3 | 140.5 | 143.6 | 151.5 | 157.9 | 162.8 | 162.7 | 158.8 | 174.4 | 170.6 | 169.4 | 159.7 |
Evaporated tinned milk (peak), 170 g | 157.7 | 166.1 | 177.1 | 197.2 | 198.4 | 194.7 | 195.9 | 190.5 | 206.0 | 198.7 | 196.0 | 191.4 |
Frozen chicken | 1419.8 | 1429.0 | 1555.5 | 1606.6 | 1606.0 | 1645.3 | 1623.8 | 1529.3 | 1570.3 | 1580.1 | 1571.5 | 1708.6 |
Gari white, sold loose | 219.6 | 260.9 | 273.7 | 288.5 | 293.0 | 315.6 | 317.1 | 310.1 | 302.0 | 268.1 | 251.9 | 199.7 |
Gari yellow, sold loose | 255.8 | 250.5 | 302.0 | 320.9 | 326.8 | 354.6 | 350.5 | 345.8 | 335.9 | 305.0 | 289.2 | 219.8 |
Groundnut oil: 1 bottle, specify bottle | 477.9 | 482.9 | 494.1 | 494.2 | 500.2 | 503.3 | 505.1 | 508.3 | 478.0 | 504.0 | 503.8 | 660.4 |
Iced sardine | 1880.0 | 1915.7 | 1902.2 | 1928.0 | 1919.6 | 1916.2 | 1915.2 | 1904.7 | 1878.5 | 1914.5 | 1903.7 | 1545.3 |
Irish potato | 300.9 | 307.3 | 311.8 | 318.9 | 315.9 | 319.3 | 311.4 | 310.5 | 307.5 | 291.1 | 290.6 | 314.2 |
Mackerel: frozen | 759.0 | 764.6 | 774.9 | 778.2 | 785.4 | 794.6 | 795.8 | 797.8 | 785.6 | 828.9 | 844.6 | 1015.6 |
Maize grain: white, sold loose | 167.1 | 172.9 | 174.0 | 182.9 | 188.9 | 190.3 | 191.3 | 192.4 | 168.1 | 191.2 | 185.1 | 154.4 |
Maize grain: yellow, sold loose | 168.8 | 174.7 | 178.8 | 185.6 | 190.6 | 191.4 | 193.0 | 193.4 | 168.2 | 191.1 | 189.9 | 161.9 |
Mudfish (aro), fresh | 994.4 | 998.7 | 1079.2 | 1008.9 | 1047.3 | 1071.1 | 1080.3 | 998.6 | 1069.9 | 1074.7 | 1081.6 | 1060.0 |
Mudfish, dried | 1812.0 | 1955.1 | 2084.8 | 2319.9 | 2388.1 | 2416.3 | 2395.8 | 2161.7 | 2204.0 | 2190.2 | 2144.2 | 1621.4 |
Onion bulb | 258.9 | 241.4 | 246.9 | 205.6 | 203.6 | 214.8 | 213.1 | 236.7 | 238.3 | 217.6 | 228.5 | 311.1 |
Palm oil: 1 bottle, specify bottle | 420.6 | 434.8 | 442.0 | 452.4 | 458.2 | 471.3 | 478.8 | 492.9 | 439.9 | 473.6 | 475.5 | 551.8 |
Plantain (ripe) | 234.3 | 234.3 | 236.7 | 240.7 | 241.5 | 249.7 | 251.3 | 254.0 | 231.7 | 247.4 | 244.9 | 240.8 |
Plantain (unripe) | 212.5 | 214.4 | 215.8 | 221.8 | 224.7 | 228.5 | 223.5 | 226.3 | 216.0 | 229.9 | 233.3 | 253.1 |
Rice: agric, sold loose | 324.0 | 355.4 | 360.9 | 324.8 | 347.7 | 352.1 | 354.1 | 349.6 | 351.1 | 325.9 | 315.1 | 317.5 |
Rice: local, sold loose | 286.2 | 306.3 | 308.9 | 299.3 | 323.8 | 325.5 | 323.3 | 320.2 | 316.4 | 292.9 | 278.9 | 278.9 |
Rice: medium, grained | 312.1 | 352.7 | 378.0 | 332.8 | 350.4 | 348.2 | 344.4 | 313.5 | 339.5 | 320.6 | 309.9 | 304.9 |
Rice: imported, high-quality, sold loose | 402.0 | 410.6 | 418.7 | 388.5 | 410.5 | 415.8 | 409.2 | 384.3 | 398.0 | 368.9 | 360.8 | 371.2 |
Sweet potato | 129.4 | 127.2 | 132.9 | 132.4 | 130.3 | 135.8 | 138.0 | 138.6 | 139.0 | 115.7 | 111.4 | 120.2 |
Tilapia fish (epiya), fresh | 792.2 | 795.3 | 798.1 | 800.8 | 805.2 | 817.6 | 823.3 | 820.3 | 795.6 | 822.4 | 813.8 | 1158.8 |
Titus, frozen | 884.8 | 942.2 | 964.6 | 935.0 | 969.6 | 1012.2 | 1014.5 | 998.0 | 974.0 | 1050.4 | 1125.9 | 1109.2 |
Tomato | 247.5 | 236.6 | 268.6 | 285.7 | 339.7 | 375.0 | 394.1 | 431.3 | 322.4 | 291.5 | 286.4 | 276.2 |
Vegetable oil: 1 bottle, specify bottle | 495.3 | 507.4 | 513.4 | 525.7 | 524.8 | 546.3 | 552.9 | 559.0 | 505.5 | 547.1 | 540.2 | 565.0 |
Wheat flour: prepacked (golden penny 2 kg) | 626.5 | 621.4 | 623.1 | 627.3 | 646.2 | 632.1 | 630.5 | 647.3 | 627.0 | 641.4 | 649.2 | 639.2 |
Yam tuber | 210.6 | 215.6 | 255.9 | 250.3 | 279.2 | 292.1 | 294.1 | 343.4 | 259.5 | 223.6 | 212.3 | 211.6 |
Prices of items of various agricultural/food commodities in 2017 (January to December).
Source: Authors.
In Table 3, prices of agriculture commodities resulting from production are presented. Such commodities include eggs; beans: brown; beef; rice (Ofada); catfish (obokun), fresh; catfish, dried; catfish, smoked; chicken feet; chicken wings; dried fish sardine; evaporated tinned milk carnation 170 g; and evaporated tinned milk (peak) 170 g. Frozen chicken; gari, white, sold loose; gari, yellow; groundnut oil; iced sardine; Irish potato; mackerel; maize grain; mudfish (aro), fresh; mudfish, dried; onion bulb; palm oil; plantain (ripe); plantain (unripe); sweet potato; tilapia fish (epiya) fresh; titus (frozen); tomato; vegetable oil; wheat flour, prepacked (golden penny 2 kg); and yam tuber, among other commodities not included. The prices of those commodities vary from January to December in 2017. This is also similar in 2018 as presented in Table 3.
Item labels | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Agric eggs (medium size price of one) | 38.8 | 41.3 | 41.2 | 42.2 | 41.8 | 41.4 | 41.2 | 41.8 | 41.2 | 41.7 | 42.4 | 42.6 |
Beans: brown, sold loose | 385.5 | 387.5 | 395.3 | 395.2 | 407.7 | 418.8 | 411.0 | 410.2 | 416.1 | 411.0 | 396.7 | 386.8 |
Beans: white black eye, sold loose | 344.5 | 348.6 | 357.0 | 355.3 | 367.2 | 369.4 | 361.3 | 361.7 | 362.7 | 354.2 | 346.4 | 344.7 |
Beef bone in | 1045.6 | 1017.3 | 1024.6 | 980.3 | 1011.1 | 1034.6 | 1028.1 | 1061.8 | 1027.7 | 1003.3 | 998.7 | 997.7 |
Beef, boneless | 1298.7 | 1274.7 | 1262.5 | 1257.7 | 1275.8 | 1271.5 | 1262.7 | 1281.0 | 1281.6 | 1260.6 | 1251.4 | 1270.8 |
Bread: sliced, 500 g | 313.4 | 314.2 | 305.9 | 306.5 | 306.0 | 308.0 | 305.3 | 308.0 | 303.8 | 299.2 | 305.5 | 296.9 |
Bread: unsliced, 500 g | 282.1 | 284.4 | 278.6 | 278.6 | 278.1 | 275.6 | 272.7 | 278.3 | 278.6 | 272.9 | 277.9 | 271.1 |
Broken rice (Ofada) | 413.2 | 405.7 | 399.5 | 400.9 | 405.0 | 404.4 | 403.6 | 397.3 | 384.5 | 370.2 | 385.6 | 381.6 |
Catfish (obokun), fresh | 1014.6 | 1037.3 | 1008.3 | 993.5 | 1013.1 | 1062.4 | 1064.7 | 1088.9 | 1089.3 | 1080.4 | 1112.1 | 1060.8 |
Catfish, dried | 1852.9 | 1857.2 | 1793.6 | 1753.5 | 1779.7 | 1770.5 | 1792.1 | 1807.1 | 1766.5 | 2362.3 | 1815.0 | 1808.6 |
Catfish, smoked | 1540.8 | 1896.9 | 1524.7 | 1514.5 | 1520.9 | 1527.8 | 1504.4 | 1512.4 | 1499.6 | 1482.3 | 1490.8 | 1519.8 |
Chicken feet | 850.0 | 778.6 | 773.0 | 758.9 | 782.5 | 784.8 | 930.5 | 803.0 | 808.1 | 775.7 | 985.3 | 737.8 |
Chicken wings | 950.8 | 955.0 | 935.8 | 921.5 | 925.5 | 960.6 | 963.0 | 992.5 | 1012.5 | 982.5 | 981.5 | 944.0 |
Dried fish sardine | 1455.5 | 1405.7 | 1410.1 | 1371.8 | 1400.0 | 1378.6 | 1395.4 | 1382.9 | 1374.3 | 1365.7 | 1331.6 | 1343.0 |
Evaporated tinned milk carnation, 170 g | 172.2 | 166.5 | 158.5 | 160.2 | 165.3 | 166.2 | 165.7 | 163.0 | 166.3 | 163.3 | 162.4 | 163.3 |
Evaporated tinned milk (peak), 170 g | 196.7 | 191.1 | 187.6 | 188.1 | 192.9 | 189.9 | 187.8 | 184.2 | 189.3 | 187.2 | 187.6 | 190.0 |
Frozen chicken | 1547.5 | 1548.9 | 1544.9 | 1537.9 | 1536.5 | 1537.2 | 1541.4 | 1533.2 | 1534.2 | 1558.2 | 1551.2 | 1625.1 |
Gari: white, sold loose | 229.3 | 210.2 | 209.1 | 208.2 | 216.0 | 216.7 | 200.7 | 198.8 | 195.9 | 188.4 | 183.6 | 166.6 |
Gari: yellow, sold loose | 271.3 | 250.1 | 244.0 | 246.5 | 256.5 | 259.8 | 248.7 | 246.8 | 253.7 | 236.3 | 222.4 | 196.0 |
Groundnut oil: 1 bottle, specify bottle | 528.6 | 571.1 | 585.2 | 584.5 | 601.5 | 612.4 | 603.6 | 603.4 | 608.4 | 592.5 | 603.9 | 585.5 |
Iced sardine | 861.1 | 885.1 | 899.7 | 907.9 | 941.5 | 923.5 | 940.5 | 937.2 | 931.6 | 916.8 | 921.0 | 906.4 |
Irish potato | 232.9 | 250.9 | 261.8 | 282.1 | 300.6 | 297.1 | 296.6 | 309.5 | 291.9 | 300.1 | 330.1 | 298.6 |
Mackerel, frozen | 915.3 | 875.3 | 915.9 | 896.4 | 927.2 | 908.9 | 922.9 | 926.2 | 944.5 | 921.0 | 937.8 | 934.2 |
Maize grain: white, sold loose | 190.5 | 185.2 | 231.6 | 173.5 | 180.5 | 183.7 | 179.4 | 180.1 | 179.9 | 171.1 | 165.0 | 161.5 |
Maize grain: yellow, sold loose | 180.0 | 199.8 | 193.8 | 191.1 | 195.7 | 197.3 | 190.4 | 191.1 | 189.8 | 178.4 | 168.9 | 160.4 |
Mudfish (aro), fresh | 1099.8 | 1098.6 | 1069.9 | 1039.0 | 1062.7 | 1070.6 | 1065.6 | 1104.8 | 1072.1 | 1052.1 | 1067.1 | 1032.2 |
Mudfish, dried | 2134.7 | 2066.0 | 1945.3 | 1912.7 | 1928.2 | 1941.9 | 1907.2 | 1897.1 | 1908.0 | 1874.4 | 1894.3 | 1861.7 |
Onion bulb | 248.7 | 252.0 | 235.3 | 234.4 | 234.0 | 240.0 | 246.9 | 252.1 | 251.3 | 232.8 | 287.7 | 259.4 |
Palm oil: 1 bottle, specify bottle | 480.9 | 510.1 | 492.1 | 486.8 | 494.9 | 500.0 | 496.4 | 501.1 | 501.0 | 495.1 | 496.2 | 474.5 |
Plantain (ripe) | 273.7 | 257.6 | 259.8 | 262.3 | 273.5 | 276.6 | 270.8 | 275.8 | 272.6 | 245.6 | 254.1 | 227.3 |
Plantain (unripe) | 248.7 | 232.2 | 242.5 | 235.7 | 247.1 | 248.5 | 248.6 | 247.1 | 244.3 | 226.6 | 225.5 | 215.7 |
Rice: agric, sold loose | 322.8 | 322.8 | 326.6 | 322.5 | 327.8 | 331.1 | 325.1 | 327.0 | 328.4 | 323.3 | 329.1 | 321.6 |
Rice, local, sold loose | 274.7 | 276.3 | 283.6 | 281.5 | 286.2 | 280.8 | 276.3 | 280.3 | 277.8 | 277.9 | 278.5 | 280.8 |
Rice: medium grained | 308.5 | 314.8 | 317.2 | 323.6 | 325.4 | 323.1 | 319.4 | 322.5 | 319.5 | 314.9 | 318.9 | 318.0 |
Rice: imported, high-quality sold loose | 360.8 | 365.2 | 363.3 | 369.4 | 374.6 | 373.5 | 370.8 | 375.0 | 371.3 | 373.0 | 376.6 | 370.6 |
Sweet potato | 113.4 | 126.4 | 130.3 | 137.4 | 150.5 | 148.7 | 163.3 | 167.1 | 167.3 | 154.7 | 147.7 | 140.7 |
Tilapia fish (epiya), fresh | 864.0 | 885.7 | 890.1 | 889.5 | 924.1 | 939.4 | 935.2 | 947.8 | 979.2 | 923.1 | 934.1 | 887.5 |
Titus, frozen | 894.7 | 901.5 | 905.8 | 898.6 | 921.0 | 950.3 | 924.8 | 946.5 | 941.5 | 923.9 | 1105.2 | 926.4 |
Tomato | 272.0 | 267.1 | 267.1 | 289.5 | 307.1 | 317.7 | 336.3 | 336.7 | 328.3 | 306.6 | 294.4 | 271.5 |
Vegetable oil: 1 bottle, specify bottle | 540.3 | 549.0 | 533.5 | 548.0 | 553.2 | 552.1 | 547.9 | 540.4 | 546.8 | 536.1 | 536.5 | 507.8 |
Wheat flour, prepacked (golden penny 2 kg) | 655.1 | 646.0 | 657.6 | 649.9 | 659.5 | 660.1 | 657.6 | 661.1 | 651.9 | 656.8 | 659.6 | 662.9 |
Yam tuber | 226.5 | 230.9 | 254.5 | 279.6 | 291.3 | 285.8 | 280.8 | 293.0 | 280.3 | 252.2 | 239.2 | 212.7 |
Prices of items of various agricultural/food commodities in 2018.
Source: Authors.
Table 4 presents the area of crops harvested (ha), yield of crop production (hg/ha) and output level of various crops (tonnes) from 1980 to 2015.
Item | Element | Unit | 1980 | 1985 | 1986 | 1990 | 1991 | 1995 | 2000 | 2005 | 2010 | 2015 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Bast fibres, other | Area harvested | ha | 1000 | 1000 | 1107 | 1000 | 1000 | 1000 | 1000 | 1000 | 1008 | |
Bast fibres, other | Yield | hg/ha | 6000 | 7000 | 7859 | 8800 | 9712 | 10,992 | 12,193 | 13,310 | 14,388 | |
Bast fibres, other | Production | tonnes | 600 | 700 | 870 | 880 | 971 | 1099 | 1219 | 1331 | 1451 | |
Carrots and turnips | Area harvested | ha | 20,000 | 20,000 | 20,000 | 22,000 | 22,303 | 24,285 | 26,492 | 27,750 | 25,300 | 25,704 |
Carrots and turnips | Yield | hg/ha | 75,000 | 75,000 | 75,000 | 78,636 | 78,464 | 81,531 | 85,097 | 85,586 | 86,921 | 88,010 |
Carrots and turnips | Production | tonnes | 150,000 | 150,000 | 150,000 | 173,000 | 175,000 | 198,000 | 225,440 | 237,500 | 219,911 | 226,222 |
Cashew nuts, with shell | Area harvested | ha | 40,000 | 40,000 | 40,000 | 50,000 | 75,000 | 155,000 | 259,000 | 309,000 | 382,509 | 131,529 |
Cashew nuts, with shell | Yield | hg/ha | 6250 | 6250 | 6250 | 6000 | 6000 | 6129 | 17,992 | 19,223 | 20,698 | 7386 |
Cashew nuts, with shell | Production | tonnes | 25,000 | 25,000 | 25,000 | 30,000 | 45,000 | 95,000 | 466,000 | 594,000 | 791,726 | 97,149 |
Cassava | Area harvested | ha | 1,200,000 | 1,075,000 | 1,095,000 | 1,634,130 | 2,551,000 | 2,944,000 | 3,300,000 | 3,782,000 | 3,481,900 | 6,216,434 |
Cassava | Yield | hg/ha | 95,833 | 112,465 | 113,132 | 116,533 | 101,936 | 106,671 | 97,000 | 109,902 | 122,155 | 92,727 |
Cassava | Production | tonnes | 11,500,000 | 12,090,000 | 12,388,000 | 19,043,008 | 26,004,000 | 31,404,000 | 32,010,000 | 41,565,000 | 42,533,180 | 57,643,271 |
Chillies and peppers, dry | Area harvested | ha | 25,700 | 27,200 | 27,500 | 28,700 | 29,000 | 30,200 | 30,410 | 30,750 | 34,000 | 38,077 |
Chillies and peppers, dry | Yield | hg/ha | 15,370 | 15,441 | 15,455 | 15,505 | 15,517 | 15,563 | 15,624 | 15,610 | 16,765 | 17,077 |
Chillies and peppers, dry | Production | tonnes | 39,500 | 42,000 | 42,500 | 44,500 | 45,000 | 47,000 | 47,512 | 48,000 | 57,000 | 65,022 |
Chillies and peppers, green | Area harvested | ha | 69,000 | 80,000 | 80,000 | 89,048 | 75,000 | 72,766 | 88,706 | 91,500 | 94,808 | 96,625 |
Chillies and peppers, green | Yield | hg/ha | 91,304 | 87,500 | 90,000 | 84,224 | 86,667 | 84,105 | 80,677 | 78,798 | 77,381 | 76,941 |
Chillies and peppers, green | Production | tonnes | 630,000 | 700,000 | 720,000 | 750,000 | 650,000 | 612,000 | 715,657 | 721,000 | 733,631 | 743,442 |
Cocoa, beans | Area harvested | ha | 700,000 | 700,000 | 700,000 | 715,000 | 726,000 | 788,000 | 966,000 | 1,198,902 | 1,272,430 | 1,057,174 |
Cocoa, beans | Yield | hg/ha | 2186 | 2286 | 2114 | 3413 | 3691 | 2576 | 3499 | 3678 | 3137 | 2857 |
Cocoa, beans | Production | tonnes | 153,000 | 160,000 | 148,000 | 244,000 | 268,000 | 203,000 | 338,000 | 441,000 | 399,200 | 302,066 |
Coconuts | Area harvested | ha | 32,000 | 34,500 | 34,500 | 35,500 | 37,000 | 28,500 | 36,000 | 39,000 | 39,000 | 38,701 |
Coconuts | Yield | hg/ha | 28,125 | 29,565 | 30,145 | 33,239 | 34,865 | 52,281 | 44,444 | 53,590 | 67,645 | 69,744 |
Coconuts | Production | tonnes | 90,000 | 102,000 | 104,000 | 118,000 | 129,000 | 149,000 | 160,000 | 209,000 | 263,815 | 269,920 |
Coffee, green | Area harvested | ha | 7000 | 12,000 | 2400 | 3434 | 3500 | 3122 | 3190 | 3670 | 1990 | 1534 |
Coffee, green | Yield | hg/ha | 5000 | 5000 | 5000 | 8824 | 9143 | 9896 | 12,006 | 13,597 | 12,063 | 12,899 |
Coffee, green | Production | tonnes | 3500 | 6000 | 1200 | 3030 | 3200 | 3090 | 3830 | 4990 | 2400 | 1979 |
Cotton lint | Production | tonnes | 29,324 | 10,524 | 36,290 | 95,000 | 103,000 | 95,000 | 147,000 | 190,000 | 220,000 | |
Cottonseed | Production | tonnes | 55,075 | 24,000 | 63,000 | 180,000 | 195,000 | 153,000 | 247,000 | 323,000 | 370,000 | |
Cow peas, dry | Area harvested | ha | 1,463,000 | 1,405,000 | 1,405,000 | 1,805,000 | 1,885,740 | 3,585,000 | 3,583,000 | 4,140,000 | 2,859,760 | 3,635,700 |
Cow peas, dry | Yield | hg/ha | 3486 | 4349 | 4569 | 7490 | 7180 | 4884 | 6001 | 6800 | 11,778 | 6343 |
Cow peas, dry | Production | tonnes | 510,000 | 611,000 | 642,000 | 1,352,000 | 1,354,000 | 1,751,000 | 2,150,000 | 2,815,000 | 3,368,250 | 2,306,200 |
Fibre crops nes | Production | tonnes | 11 | 12 | ||||||||
Fonio | Area harvested | ha | 30,000 | 38,000 | 44,000 | 65,000 | 72,000 | 108,000 | 133,000 | 198,000 | 151,766 | 187,560 |
Fonio | Yield | hg/ha | 6000 | 6579 | 6136 | 6000 | 5972 | 5370 | 5714 | 4798 | 5211 | 4405 |
Fonio | Production | tonnes | 18,000 | 25,000 | 27,000 | 39,000 | 43,000 | 58,000 | 76,000 | 95,000 | 79,087 | 82,617 |
Fruit, citrus nes | Area harvested | ha | 550,000 | 570,000 | 570,000 | 580,000 | 630,000 | 643,589 | 727,596 | 731,000 | 790,000 | 821,533 |
Fruit, citrus nes | Yield | hg/ha | 32,727 | 35,088 | 35,088 | 35,914 | 39,683 | 42,263 | 44,668 | 45,179 | 48,101 | 48,757 |
Fruit, citrus nes | Production | tonnes | 1,800,000 | 2,000,000 | 2,000,000 | 2,083,000 | 2,500,000 | 2,720,000 | 3,250,000 | 3,302,611 | 3,800,000 | 4,005,520 |
Fruit, fresh nes | Area harvested | ha | 145,000 | 184,500 | 196,000 | 197,349 | 208,520 | 238,082 | 284,711 | 218,500 | 177,000 | 180,210 |
Fruit, fresh nes | Yield | hg/ha | 64,138 | 65,041 | 66,327 | 65,873 | 63,337 | 62,902 | 63,575 | 64,119 | 67,797 | 68,225 |
Fruit, fresh nes | Production | tonnes | 930,000 | 1,200,000 | 1,300,000 | 1,300,000 | 1,320,713 | 1,497,578 | 1,810,060 | 1,401,000 | 1,200,000 | 1,229,484 |
Garlic | Production | tonnes | 587 | 800 | ||||||||
Ginger | Area harvested | ha | 400 | 16,000 | 30,000 | 84,000 | 100,000 | 148,000 | 158,000 | 181,000 | 52,330 | 64,356 |
Ginger | Yield | hg/ha | 5000 | 5000 | 5000 | 5000 | 5000 | 5338 | 6203 | 6906 | 31,000 | 44,198 |
Ginger | Production | tonnes | 200 | 8000 | 15,000 | 42,000 | 50,000 | 79,000 | 98,000 | 125,000 | 162,223 | 284,440 |
Groundnuts, with shell | Area harvested | ha | 563,000 | 594,000 | 793,000 | 707,000 | 112,7000 | 1,767,000 | 1,934,000 | 2,187,000 | 2,789,180 | 2,801,756 |
Groundnuts, with shell | Yield | hg/ha | 8366 | 10,455 | 11,299 | 16,492 | 12,076 | 8936 | 15,000 | 15,903 | 13,621 | 12,376 |
Groundnuts, with shell | Production | tonnes | 471,000 | 621,000 | 896,000 | 1,166,000 | 1,361,000 | 1,579,000 | 2,901,000 | 3,478,000 | 3,799,240 | 3,467,446 |
Karite nuts (shea nuts) | Area harvested | ha | 100,000 | 94,000 | 87,000 | 184,000 | 204,000 | 235,000 | 232,000 | 257,239 | 342,750 | 409,963 |
Karite nuts (shea nuts) | Yield | hg/ha | 11,000 | 10,638 | 11,839 | 15,707 | 15,980 | 16,340 | 15,905 | 15,940 | 9500 | 8786 |
Karite nuts (shea nuts) | Production | tonnes | 110,000 | 100,000 | 103,000 | 289,000 | 326,000 | 384,000 | 369,000 | 410,029 | 325,610 | 360,177 |
Kola nuts | Area harvested | ha | 140,000 | 175,000 | 175,000 | 125,000 | 130,000 | 105,000 | 91,000 | 94,250 | 270,143 | 244,705 |
Kola nuts | Yield | hg/ha | 9643 | 9829 | 9943 | 10,800 | 12,692 | 9048 | 9011 | 9045 | 5366 | 6608 |
Kola nuts | Production | tonnes | 135,000 | 172,000 | 174,000 | 135,000 | 165,000 | 95,000 | 82,000 | 85,250 | 144,950 | 161,711 |
Maize | Area harvested | ha | 465,000 | 1,556,000 | 2,800,000 | 5,104,000 | 5,142,000 | 5,472,000 | 3,159,000 | 3,589,000 | 4,149,310 | 6,771,189 |
Maize | Yield | hg/ha | 13,161 | 11,735 | 12,679 | 11,301 | 11,299 | 12,666 | 13,001 | 16,598 | 18,502 | 15,599 |
Maize | Production | tonnes | 612,000 | 1,826,000 | 3,550,000 | 5,768,000 | 5,810,000 | 6,931,000 | 4,107,000 | 5,957,000 | 7,676,850 | 10,562,050 |
Maize, green | Area harvested | ha | 46,000 | 156,000 | 172,000 | 150,000 | 155,000 | 167,706 | 162,619 | 161,500 | 183,916 | 200,356 |
Maize, green | Yield | hg/ha | 35,000 | 29,231 | 25,233 | 30,533 | 30,645 | 33,704 | 34,662 | 35,697 | 36,774 | 37,699 |
Maize, green | Production | tonnes | 161,000 | 456,000 | 434,000 | 458,000 | 475,000 | 565,240 | 563,667 | 576,500 | 676,338 | 755,319 |
Mangoes, mangosteens, guavas | Area harvested | ha | 80,000 | 80,000 | 80,000 | 85,000 | 88,000 | 106,000 | 125,000 | 125,500 | 130,000 | 131,132 |
Mangoes, mangosteens, guavas | Yield | hg/ha | 50,000 | 50,000 | 50,000 | 59,294 | 59,091 | 59,528 | 58,400 | 58,247 | 65,385 | 68,239 |
Mangoes, mangosteens, guavas | Production | tonnes | 400,000 | 400,000 | 400,000 | 504,000 | 520,000 | 631,000 | 730,000 | 731,000 | 850,000 | 894,833 |
Melon seed | Area harvested | ha | 76,000 | 183,000 | 150,000 | 230,000 | 231,000 | 285,000 | 575,000 | 694,000 | 469,690 | 967,937 |
Melon seed | Yield | hg/ha | 12,368 | 8033 | 10,200 | 9043 | 9481 | 10,070 | 6000 | 6499 | 10,802 | 5758 |
Melon seed | Production | tonnes | 94,000 | 147,000 | 153,000 | 208,000 | 219,000 | 287,000 | 345,000 | 451,000 | 507,340 | 557,328 |
Millet | Area harvested | ha | 2,824,000 | 2,346,000 | 3,917,000 | 4,778,000 | 4,560,000 | 5,107,000 | 5,814,000 | 4,685,000 | 4,364,140 | 1,591,803 |
Millet | Yield | hg/ha | 8336 | 15,277 | 10,495 | 10,749 | 9011 | 10,893 | 10,501 | 15,300 | 11,848 | 9331 |
Millet | Production | tonnes | 2,354,000 | 3,584,000 | 4,111,000 | 5,136,000 | 4,109,000 | 5,563,000 | 6,105,000 | 7,168,000 | 5,170,430 | 1,485,387 |
Nuts, nes | Area harvested | ha | 100 | 300 | 2500 | 2500 | 5421 | 2965 | 2550 | 2800 | 2799 | |
Nuts, nes | Yield | hg/ha | 20,000 | 20,000 | 24,000 | 20,000 | 6118 | 17,416 | 20,196 | 25,000 | 25,915 | |
Nuts, nes | Production | tonnes | 200 | 600 | 6000 | 5000 | 3316 | 5163 | 5150 | 7000 | 7253 | |
Oil palm fruit | Area harvested | ha | 2,300,000 | 2,200,000 | 2,220,000 | 2,300,000 | 2,450,000 | 2,938,000 | 3,080,000 | 3,350,000 | 3,200,000 | 3,076,881 |
Oil palm fruit | Yield | hg/ha | 25,000 | 25,909 | 26,577 | 26,957 | 26,531 | 26,549 | 26,688 | 25,373 | 25,000 | 25,683 |
Oil palm fruit | Production | tonnes | 5,750,000 | 5,700,000 | 5,900,000 | 6,200,000 | 6,500,000 | 7,800,000 | 8,220,000 | 8,500,000 | 8,000,000 | 7,902,277 |
Oil, palm | Production | tonnes | 650,000 | 615,000 | 650,000 | 730,000 | 760,000 | 860,000 | 899,000 | 1,170,000 | 970,820 | |
Oilseeds nes | Production | tonnes | 548 | 759 | 820 | 596 | 700 | 814 | 600 | |||
Okra | Area harvested | ha | 200,000 | 230,000 | 250,000 | 260,000 | 231,278 | 259,393 | 292,135 | 350,000 | 397,290 | 1,859,900 |
Okra | Yield | hg/ha | 21,000 | 20,652 | 19,600 | 20,231 | 22,916 | 24,287 | 25,719 | 27,143 | 27,275 | 11,118 |
Okra | Production | tonnes | 420,000 | 475,000 | 490,000 | 526,000 | 530,000 | 630,000 | 751,342 | 950,000 | 1,083,620 | 2,067,900 |
Onions, dry | Area harvested | ha | 30,000 | 30,000 | 35,000 | 36,667 | 63,403 | 87,996 | 115,501 | 264,174 | 179,984 | 434,500 |
Onions, dry | Yield | hg/ha | 133,333 | 133,333 | 142,857 | 137,649 | 60,904 | 56,654 | 51,342 | 44,763 | 74,797 | 22,967 |
Onions, dry | Production | tonnes | 400,000 | 400,000 | 500,000 | 504,719 | 386,152 | 498,539 | 593,008 | 1,182,520 | 1,346,218 | 997,900 |
Onions, shallots, green | Area harvested | ha | 10,000 | 10,000 | 10,000 | 10,476 | 6046 | 10,128 | 10,794 | 11,250 | 13,232 | 14,366 |
Onions, shallots, green | Yield | hg/ha | 100,000 | 100,000 | 105,000 | 119,320 | 223,291 | 215,057 | 206,277 | 196,000 | 175,089 | 163,848 |
Onions, shallots, green | Production | tonnes | 100,000 | 100,000 | 105,000 | 125,000 | 135,000 | 217,815 | 222,656 | 220,500 | 231,684 | 235,383 |
Palm kernels | Area harvested | ha | 450,000 | |||||||||
Palm kernels | Production | tonnes | 279,000 | 360,000 | 355,000 | 356,000 | 369,000 | 543,000 | 577,000 | 465,000 | 233,000 | |
Papayas | Area harvested | ha | 55,000 | 55,000 | 55,000 | 65,000 | 66,000 | 80,000 | 89,315 | 91,500 | 92,865 | 93,445 |
Papayas | Yield | hg/ha | 72,727 | 72,727 | 72,727 | 79,538 | 80,303 | 81,000 | 83,371 | 82,568 | 80,763 | 93,680 |
Papayas | Production | tonnes | 400,000 | 400,000 | 400,000 | 517,000 | 530,000 | 648,000 | 744,626 | 755,500 | 750,000 | 875,401 |
Pineapples | Area harvested | ha | 95,000 | 95,000 | 95,000 | 100,000 | 105,505 | 105,802 | 117,005 | 116,500 | 180,000 | 184,551 |
Pineapples | Yield | hg/ha | 63,158 | 73,684 | 73,684 | 76,300 | 75,825 | 75,613 | 75,733 | 76,395 | 82,631 | 81,270 |
Pineapples | Production | tonnes | 600,000 | 700,000 | 700,000 | 763,000 | 800,000 | 800,000 | 886,110 | 890,000 | 1,487,350 | 1,499,840 |
Plantains and others | Area harvested | ha | 180,000 | 185,500 | 187,200 | 162,000 | 178,000 | 250,000 | 386,000 | 447,000 | 449,220 | 486,048 |
Plantains and others | Yield | hg/ha | 57,889 | 60,000 | 60,203 | 75,000 | 75,225 | 65,280 | 51,010 | 57,964 | 59,559 | 62,977 |
Plantains and others | Production | tonnes | 1,042,000 | 1,113,000 | 1,127,000 | 1,215,000 | 1,339,000 | 1,632,000 | 1,969,000 | 2,591,000 | 2,675,530 | 3,060,962 |
Potatoes | Area harvested | ha | 5500 | 7000 | 7600 | 7700 | 9400 | 13,600 | 212,000 | 260,000 | 265,992 | 328,009 |
Potatoes | Yield | hg/ha | 72,727 | 61,429 | 60,526 | 70,130 | 70,213 | 69,853 | 28,255 | 29,846 | 38,584 | 36,727 |
Potatoes | Production | tonnes | 40,000 | 43,000 | 46,000 | 54,000 | 66,000 | 95,000 | 599,000 | 776,000 | 102,6311 | 120,4676 |
Pulses, nes | Area harvested | ha | 115,000 | 120,000 | 120,000 | 131,832 | 74,309 | 87,216 | 102,492 | 119,432 | 130,000 | 140,095 |
Pulses, nes | Yield | hg/ha | 4609 | 4167 | 4167 | 4162 | 3870 | 3966 | 4087 | 4104 | 4308 | 4609 |
Pulses, nes | Production | tonnes | 53,000 | 50,000 | 50,000 | 54,870 | 28,756 | 34,592 | 41,887 | 49,018 | 56,000 | 64,570 |
Rice, paddy | Area harvested | ha | 550,000 | 670,000 | 700,000 | 1,208,000 | 1,652,000 | 1,796,000 | 2,199,000 | 2,494,000 | 2,432,630 | 3,121,562 |
Rice, paddy | Yield | hg/ha | 19,818 | 21,343 | 20,233 | 20,695 | 19,528 | 16,258 | 14,998 | 14,302 | 18,386 | 20,042 |
Rice, paddy | Production | tonnes | 1,090,000 | 1,430,000 | 1,416,322 | 2,500,000 | 3,226,000 | 2,920,000 | 3,298,000 | 3,567,000 | 4,472,520 | 6,256,228 |
Rubber, natural | Area harvested | ha | 73,000 | 73,000 | 73,000 | 22,5000 | 268,000 | 297,000 | 330,000 | 339,500 | 360,541 | 365,622 |
Rubber, natural | Yield | hg/ha | 6164 | 8219 | 8219 | 6533 | 5784 | 4209 | 3242 | 3998 | 4019 | 4228 |
Rubber, natural | Production | tonnes | 45,000 | 60,000 | 60,000 | 147,000 | 155,000 | 125,000 | 107,000 | 135,716 | 144,912 | 154,571 |
Seed cotton | Area harvested | ha | 476,000 | 220,000 | 285,000 | 575,000 | 643,000 | 431,000 | 538,000 | 659,000 | 398,570 | 401,441 |
Seed cotton | Yield | hg/ha | 1801 | 1591 | 3509 | 4800 | 4806 | 5824 | 7416 | 7906 | 15,115 | 6913 |
Seed cotton | Production | tonnes | 85,733 | 35,000 | 100,000 | 276,000 | 309,000 | 251,000 | 399,000 | 521,000 | 602,440 | 277,523 |
Sesame seed | Area harvested | ha | 135,000 | 116,700 | 104,700 | 110,000 | 115,000 | 133,000 | 151,000 | 196,000 | 324,570 | 329,460 |
Sesame seed | Yield | hg/ha | 3037 | 2999 | 3343 | 4000 | 4000 | 4511 | 4768 | 5102 | 4603 | 5218 |
Sesame seed | Production | tonnes | 41,000 | 35,000 | 35,000 | 44,000 | 46,000 | 60,000 | 72,000 | 100,000 | 149,410 | 171,900 |
Sorghum | Area harvested | ha | 3,286,000 | 4,862,000 | 5,147,000 | 4,185,000 | 5,538,000 | 6,095,000 | 6,885,000 | 7,284,000 | 4,960,130 | 5,899,134 |
Sorghum | Yield | hg/ha | 11,229 | 10,101 | 10,540 | 10,000 | 9691 | 11,480 | 11,200 | 12,600 | 14,397 | 11,875 |
Sorghum | Production | tonnes | 3,690,000 | 4,911,000 | 5,425,000 | 4,185,000 | 5,367,000 | 6,997,000 | 7,711,000 | 9,178,000 | 7,140,970 | 7,005,025 |
Soybeans | Area harvested | ha | 270,000 | 205,000 | 210,000 | 729,000 | 468,000 | 617,000 | 517,000 | 601,000 | 281,890 | 609,333 |
Soybeans | Yield | hg/ha | 2778 | 2927 | 3238 | 2990 | 3098 | 4652 | 8298 | 9401 | 12,951 | 9658 |
Soybeans | Production | tonnes | 75,000 | 60,000 | 68,000 | 218,000 | 145,000 | 287,000 | 429,000 | 565,000 | 365,080 | 588,523 |
Spices, nes | Area harvested | ha | 2900 | 3000 | 3000 | 3600 | 1428 | 1971 | 2634 | 3250 | 3600 | 4087 |
Spices, nes | Yield | hg/ha | 13,793 | 14,667 | 13,333 | 13,333 | 12,962 | 13,112 | 13,299 | 13,846 | 15,278 | 15,746 |
Spices, nes | Production | tonnes | 4000 | 4400 | 4000 | 4800 | 1851 | 2585 | 3503 | 4500 | 5500 | 6436 |
Sugar cane | Area harvested | ha | 22,000 | 21,400 | 22,400 | 22,400 | 22,200 | 19,270 | 24,000 | 44,000 | 45,680 | 88,135 |
Sugar cane | Yield | hg/ha | 395,455 | 402,804 | 400,446 | 410,714 | 400,000 | 305,656 | 289,583 | 207,727 | 186,055 | 164,516 |
Sugar cane | Production | tonnes | 870,000 | 862,000 | 897,000 | 920,000 | 888,000 | 589,000 | 695,000 | 914,000 | 849,898 | 1,449,963 |
Sweet potatoes | Area harvested | ha | 12,000 | 12,000 | 13,000 | 28,000 | 31,000 | 299,000 | 823,000 | 989,000 | 1,298,486 | 1,499,015 |
Sweet potatoes | Yield | hg/ha | 83,333 | 66,667 | 63,846 | 51,071 | 59,355 | 39,064 | 29,988 | 32,406 | 26,701 | 25,652 |
Sweet potatoes | Production | tonnes | 100,000 | 80,000 | 83,000 | 143,000 | 184,000 | 1,168,000 | 2,468,000 | 3,205,000 | 3,467,073 | 3,845,248 |
Taro (cocoyam) | Area harvested | ha | 65,000 | 70,000 | 91,000 | 141,000 | 166,000 | 229,000 | 587,000 | 667,000 | 520,130 | 826,800 |
Taro (cocoyam) | Yield | hg/ha | 32,000 | 33,143 | 40,989 | 51,844 | 49,940 | 51,616 | 66,201 | 75,982 | 56,853 | 39,631 |
Taro (cocoyam) | Production | tonnes | 208,000 | 232,000 | 373,000 | 731,000 | 829,000 | 1,182,000 | 3,886,000 | 5,068,000 | 2,957,090 | 3,276,700 |
Tobacco, unmanufactured | Area harvested | ha | 20,680 | 19,500 | 20,000 | 22,000 | 18,522 | 17,463 | 37,000 | 25,000 | 14,789 | 9500 |
Tobacco, unmanufactured | Yield | hg/ha | 6286 | 5385 | 4500 | 4091 | 4859 | 5268 | 5946 | 6000 | 6131 | 5263 |
Tobacco, unmanufactured | Production | tonnes | 13,000 | 10,500 | 9000 | 9000 | 9000 | 9200 | 22,000 | 15,000 | 9066 | 5000 |
Tomatoes | Area harvested | ha | 32,500 | 35,000 | 35,500 | 37,500 | 38,000 | 55,000 | 210,000 | 250,000 | 272,950 | 557,500 |
Tomatoes | Yield | hg/ha | 100,000 | 100,000 | 100,000 | 100,000 | 100,000 | 103,455 | 60,038 | 81,714 | 65,945 | 75,862 |
Tomatoes | Production | tonnes | 325,000 | 350,000 | 355,000 | 375,000 | 380,000 | 569,000 | 1,260,794 | 2,042,861 | 1,799,960 | 4,229,330 |
Vegetables, fresh nes | Area harvested | ha | 210,000 | 260,000 | 265,000 | 350,000 | 380,000 | 440,000 | 620,754 | 725,000 | 724,335 | 753,081 |
Vegetables, fresh nes | Yield | hg/ha | 46,286 | 48,231 | 48,792 | 50,314 | 53,289 | 59,273 | 63,552 | 71,517 | 82,818 | 91,171 |
Vegetables, fresh nes | Production | tonnes | 972,000 | 1,254,000 | 1,293,000 | 1,761,000 | 2,025,000 | 2,608,000 | 3,945,000 | 5,185,000 | 5,998,811 | 6,865,947 |
Wheat | Area harvested | ha | 10,000 | 55,000 | 67,000 | 60,000 | 50,000 | 20,000 | 52,000 | 60,000 | 74,399 | 60,000 |
Wheat | Yield | hg/ha | 24,000 | 20,545 | 19,701 | 8333 | 12,000 | 21,800 | 14,038 | 11,000 | 14,844 | 10,000 |
Wheat | Production | tonnes | 24,000 | 11,3000 | 13,2000 | 50,000 | 60,000 | 43,600 | 73,000 | 66,000 | 110,441 | 60,000 |
Yams | Area harvested | ha | 498,000 | 840,000 | 924,000 | 1,276,000 | 1,639,000 | 2,118,000 | 2,647,000 | 2,957,000 | 2,868,990 | 5,389,870 |
Yams | Yield | hg/ha | 105,382 | 56,405 | 56,374 | 106,771 | 103,453 | 107,734 | 98,984 | 114,981 | 13,0109 | 84,748 |
Yams | Production | tonnes | 5,248,000 | 4,738,000 | 5,209,000 | 13,624,000 | 16,956,000 | 22,818,000 | 26,201,000 | 34,000,000 | 37,328,180 | 45,677,939 |
Cereals (rice milled eqv) | Area harvested | ha | 7,165,000 | 9,527,000 | 12,675,000 | 15,400,000 | 17,014,000 | 18,598,000 | 18,242,000 | 18,310,000 | 16,132,376 | 17,631,248 |
Cereals (rice milled eqv) | Yield | hg/ha | 10,363 | 11,979 | 11,195 | 10,939 | 10,310 | 11,582 | 11,113 | 13,568 | 14,357 | 13,254 |
Cereals (rice milled eqv) | Production | tonnes | 7,425,030 | 11,412,810 | 14,189,687 | 16,845,500 | 17,540,742 | 21,540,240 | 20,271,766 | 24,843,189 | 23,160,948 | 23,367,984 |
Cereals, total | Area harvested | ha | 7,165,000 | 9,527,000 | 12,675,000 | 15,400,000 | 17,014,000 | 18,598,000 | 18,242,000 | 18,310,000 | 16,132,376 | 17,631,248 |
Cereals, total | Yield | hg/ha | 10,870 | 12,479 | 11,567 | 11,479 | 10,941 | 12,105 | 11,715 | 14,217 | 15,280 | 14,435 |
Cereals, total | Production | tonnes | 7,788,000 | 11,889,000 | 14,661,322 | 17,678,000 | 18,615,000 | 22,512,600 | 21,370,000 | 26,031,000 | 24,650,297 | 25,451,307 |
Citrus fruit, total | Area harvested | ha | 550,000 | 570,000 | 570,000 | 580,000 | 630,000 | 643,589 | 727,596 | 731,000 | 790,000 | 821,533 |
Citrus fruit, total | Yield | hg/ha | 32,727 | 35,088 | 35,088 | 35,914 | 39,683 | 42,263 | 44,668 | 45,179 | 48,101 | 48,757 |
Citrus fruit, total | Production | tonnes | 1,800,000 | 2,000,000 | 2,000,000 | 2,083,000 | 2,500,000 | 2,720,000 | 3,250,000 | 3,302,611 | 3,800,000 | 4,005,520 |
Coarse grain, total | Area harvested | ha | 6,605,000 | 8,802,000 | 11,908,000 | 14,132,000 | 15,312,000 | 16,782,000 | 15,991,000 | 15,756,000 | 13,625,346 | 14,449,686 |
Coarse grain, total | Yield | hg/ha | 10,104 | 11,754 | 11,012 | 10,705 | 10,011 | 11,649 | 11,256 | 142,16 | 14,728 | 13,243 |
Coarse grain, total | Production | tonnes | 6,674,000 | 10,346,000 | 13,113,000 | 15,128,000 | 15,329,000 | 19,549,000 | 17,999,000 | 22,398,000 | 20,067,337 | 19,135,079 |
Fibre crops primary | Area harvested | ha | 476,000 | 221,000 | 286,000 | 576,107 | 644,000 | 432,000 | 539,000 | 660,000 | 399,570 | |
Fibre crops primary | Yield | hg/ha | 616 | 503 | 1293 | 1664 | 1613 | 2222 | 2748 | 2897 | 5539 | |
Fibre crops primary | Production | tonnes | 29,324 | 11,124 | 36,990 | 95,881 | 103,892 | 95,971 | 148,099 | 191,219 | 221,331 | |
Fruit primary | Area harvested | ha | 1,105,000 | 1,170,000 | 1,183,200 | 1,189,349 | 1,276,025 | 1,423,473 | 1,729,627 | 1,730,000 | 1,819,085 | 1,896,920 |
Fruit primary | Yield | hg/ha | 46,805 | 49,684 | 50,093 | 53,660 | 54,934 | 55,699 | 54,288 | 55,902 | 59,166 | 60,973 |
Fruit primary | Production | tonnes | 5,172,000 | 5,813,000 | 5,927,000 | 6,382,000 | 7,009,713 | 7,928,578 | 9,389,796 | 9,671,111 | 10,762,880 | 11,566,040 |
Oil crops, cake equivalent | Area harvested | ha | 3,852,000 | 3,553,200 | 3,797,200 | 4,686,500 | 5,071,000 | 6,199,500 | 6,831,000 | 7,726,000 | 7,736,900 | |
Oil crops, cake equivalent | Yield | hg/ha | 1287 | 1680 | 1920 | 2239 | 2147 | 2262 | 3110 | 3253 | 3159 | |
Oil crops, cake equivalent | Production | tonnes | 495,908 | 597,030 | 728,900 | 1,049,229 | 1,088,695 | 1,402,352 | 2,124,177 | 2,513,260 | 2,444,181 | |
Oil crops, oil equivalent | Area harvested | ha | 3,952,000 | 3,647,200 | 3,884,200 | 4,870,500 | 5,275,000 | 6,434,500 | 7,063,000 | 7,983,239 | 8,052,650 | |
Oil crops, oil equivalent | Yield | hg/ha | 2599 | 2958 | 3101 | 3040 | 2994 | 2933 | 3397 | 3619 | 3322 | |
Oil crops, oil equivalent | Production | tonnes | 1,026,982 | 1,078,950 | 1,204,640 | 1,480,674 | 1,579,318 | 1,887,136 | 2,399,149 | 2,888,867 | 2,675,077 | |
Pulses, total | Area harvested | ha | 1,578,000 | 1,525,000 | 1,525,000 | 1,936,832 | 1,960,049 | 3,672,216 | 3,685,492 | 4,259,432 | 2,989,760 | 3,775,795 |
Pulses, total | Yield | hg/ha | 3568 | 4334 | 4538 | 7264 | 7055 | 4862 | 5947 | 6724 | 11,453 | 6279 |
Pulses, total | Production | tonnes | 563,000 | 661,000 | 692,000 | 1,406,870 | 1,382,756 | 1,785,592 | 2,191,887 | 2,864,018 | 3,424,250 | 2,370,770 |
Roots and tubers, total | Area harvested | ha | 1,780,500 | 2,004,000 | 2,130,600 | 3,086,830 | 4,396,400 | 5,603,600 | 7,569,000 | 8,655,000 | 8,435,498 | 14,260,128 |
Roots and tubers, total | Yield | hg/ha | 96,018 | 85,744 | 84,948 | 108,833 | 100,171 | 101,126 | 86,093 | 97,763 | 10,3505 | 78,294 |
Roots and tubers, total | Production | tonnes | 17,096,000 | 17,183,000 | 18,099,000 | 33,595,008 | 44,039,000 | 56,667,000 | 65,164,000 | 84,614,000 | 87,311,834 | 1.12E+08 |
Tree nuts, total | Area harvested | ha | 40,000 | 40,100 | 40,300 | 52,500 | 77,500 | 16,0421 | 26,1965 | 31,1550 | 38,5309 | 13,4328 |
Tree nuts, total | Yield | hg/ha | 6250 | 6284 | 6352 | 6857 | 6452 | 6129 | 17,986 | 19,231 | 20,730 | 7772 |
Tree nuts, total | Production | tonnes | 25,000 | 25,200 | 25,600 | 36,000 | 50,000 | 98,316 | 471,163 | 599,150 | 798,726 | 104,402 |
Vegetables primary | Area harvested | ha | 617,500 | 821,000 | 867,500 | 955,691 | 971,030 | 1,117,275 | 1,527,001 | 1,881,174 | 1,891,815 | 3,942,033 |
Vegetables primary | Yield | hg/ha | 51,142 | 47,320 | 46,651 | 48,894 | 48,980 | 52,794 | 54,208 | 59,090 | 63,911 | 40,898 |
Vegetables primary | Production | tonnes | 3,158,000 | 3,885,000 | 4,047,000 | 4,672,719 | 4,756,152 | 5,898,594 | 8,277,564 | 11,115,881 | 12,090,760 | 16,122,242 |
Crop production in Nigeria (1980–2015): harvest area, yield and production.
Source: Authors.
Various crops presented in Table 4 include bast fibres, carrots and turnips, cashew nuts (with shell), cassava, chillies and peppers (dry), chillies and peppers (green), cocoa, beans, coconuts, coffee (green), cotton (lint), cottonseed, cow peas (dry), fibre crops (nes), fruit, citrus (nes), garlic, groundnuts, karite nuts (shea nuts), kola nuts, maize, maize (green), mangoes, mangosteens, guavas, melon seed, millet, nuts (nes), nuts (nes), oil palm fruit, oilseeds (nes), okra, onions (dry), shallots (green), palm kernels, papayas, potatoes, pulses (nes), rice (paddy), rubber (natural), seed cotton, sesame seed, sorghum, soybeans, spices (nes), sugar cane, sweet potatoes, taro (cocoyam), tobacco, unmanufactured, tomatoes, vegetables (fresh nes), wheat, yams, cereals (rice milled eqv), cereals (total), citrus fruit (total), coarse grain (total), fibre crops primary, fruit primary, oil crops, cake equivalent, pulses (total), roots and tubers (total), tree nuts (total), vegetables primary, etc.
Therefore, to ensure sustainable crop production, the agricultural sector needs to be invested on through various means like credit facilities and incentives such as social protection for the mitigation of risk and shocks [1]. Also, the nutritional level of plants should be improved through fertiliser application among other means to enhance crop yields [17].
The study aims at examining factors that improve agricultural production, especially crop yields that can be made possible by plant nutrients. Increase in crop production (food and cash crops) will lead to food security in the long run. The study employed a review of literature and stylised fact approach using tables to know the level of crop production in Nigeria. From the stylised facts and the reviewed literature, authors noticed that there are fluctuations of prices of food items in Nigeria.
With respect to the factors contributing to crop and agricultural production, employment in agriculture was observed to be a major factor. Also, the proportion of women in agriculture is higher than the proportion of men in agriculture; this invariably implies that women actually contribute more to production level. In this regard, to further enhance productivity, there should be equal access to production resources such land, credit facilities, access to social protection incentives to mitigate risks and shocks and more innovation and technological advancement in the agricultural sector thereby improving the sustainability of crop production.
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He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University. His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:null},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"243698",title:"M.D.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:"Shanxi Eye Hospital",institution:{name:"Shanxi Eye Hospital",country:{name:"China"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Igor Victorovich Lakhno was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics, and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. 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Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,series:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983"},editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",slug:"ana-isabel-flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",slug:"christian-palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",slug:"francisco-javier-martin-romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},onlineFirstChapters:{paginationCount:14,paginationItems:[{id:"82103",title:"The Role of Endoplasmic Reticulum Stress and Its Regulation in the Progression of Neurological and Infectious Diseases",doi:"10.5772/intechopen.105543",signatures:"Mary Dover, Michael Kishek, Miranda Eddins, Naneeta Desar, Ketema Paul and Milan Fiala",slug:"the-role-of-endoplasmic-reticulum-stress-and-its-regulation-in-the-progression-of-neurological-and-i",totalDownloads:5,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Updates on Endoplasmic Reticulum",coverURL:"https://cdn.intechopen.com/books/images_new/11674.jpg",subseries:{id:"14",title:"Cell and Molecular Biology"}}},{id:"80954",title:"Ion Channels and Neurodegenerative Disease Aging Related",doi:"10.5772/intechopen.103074",signatures:"Marika Cordaro, Salvatore Cuzzocrea and Rosanna Di Paola",slug:"ion-channels-and-neurodegenerative-disease-aging-related",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Ion Channels - From Basic Properties to Medical Treatment",coverURL:"https://cdn.intechopen.com/books/images_new/10838.jpg",subseries:{id:"14",title:"Cell and Molecular Biology"}}},{id:"81647",title:"Diabetes and Epigenetics",doi:"10.5772/intechopen.104653",signatures:"Rasha A. 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