Soil Properties and Equipments
\r\n\tThe major pathogenetic mechanisms resulting from RAAS overactivity include activation of the sympathetic nervous system, endothelial dysfunction, proinflammatory, and procoagulant states.
\r\n\tEmerging from basic science evidence, major clinical trials established the beneficial effects of inhibitors of the different components of RAAS such as angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs), aldosterone antagonists. These effects range from treatment of hypertension, diabetic nephropathy, CHF, as well as improvement of outcomes after myocardial infarction and improvement in glucose homeostasis and prevention of type 2 diabetes with some agents.
\r\n\tIn this book, written by a world-renowned scholar, we will address the major concepts and topics related to RAAS activation including the pathogenetic mechanisms underlying the deleterious effects of activated RAAS and the role of local tissue RAAS in various organ systems such as the heart and vasculature, the skeletal muscle, adipose tissues, pancreas and the angiotensinergic pathways in the brain. Cutting-edge information is provided that will address the need for a wide range of readers including a medical student, clinical practitioner, and basic science investigators alike. This book will be bridging the gap between basic science and clinical practice regarding the RAAS system, which is imminently critical and highly relevant to the practice of medicine.
\r\n\r\n\tFinally, with data emerging from the COVID-19 pandemic indicating overrepresentation of people with diseases associated with RAAS activation such as hypertension, chronic kidney disease, and diabetes, the role of RAAS activation and RAAS inhibition in the pathogenesis and clinical outcomes in COVID-19 has garnered a great deal of interest. In this book, we will dedicate a chapter addressing this topical and highly critical subject.
\r\n\t
Land degradation is a concept in which the value of the biophysical environment is affected by one or more combination of human induced processes acting upon the land. It literally refers to the impairment of natural quality of soil component of any ecosystem. Land degradation which is also seen as a decline in land quality caused by human activities, has been a major global issue since the 20th century and it has remained high on the international agenda in the 21st century. The importance of land degradation in Calabar South is enhanced because of its impact on food security and quality of the environments. The map of the study area is presented on the next page.
Land degradation can be viewed as any change or disturbance to the land perceived to be deleterious or undesirable (Eswaran, 2001). In the study area, the researcher observed loss of the biological and economic productivity and complexity of rain-fed cropland, irrigated cropland, range, forest and woodlands resulting from land uses or from a combination of processes arising from human activities and habitation patterns such as soil erosion caused by wind or water, deterioration of the physical, chemical, biological and economic properties of soil and long-term loss of natural vegetation. But there are also off-site effects, such as loss of watershed functions which is a major problem in Calabar South.
Natural hazards are excluded as a cause of land degradation in Calabar South, however human activities can indirectly affect phenomena such as floods and bush fires.
Research has shown that up to 60% of agricultural land in Calabar South is seriously degraded. Furthermore, the main outcome of land degradation is a substantial reduction in the productivity of the land as shown in figure 2
The major causes of land degradation include, land clearance poor farming practices, overgrazing, inappropriate irrigation, urban sprawl, and commercial development, land pollution including industrial waste and quarrying of stone, sand and minerals. High population density is not necessarily related to land degradation within Calabar South, but it is what a population does to the land that determines the extent of degradation. In the study area, where a large proportion of human population depend almost entirely on land resources for their sustenance, this over dependency results in the increasing competing demand for land utilization such as grazing, fish pond construction, quarrying, crop farming amongst others.
Map of Calobar South Government Area showing
Degraded agricultural land
The productivity of some land in Calabar South has declined by 60 percent as a result of soil erosion and nutrient loss (Bruinsma, 2003). Presently, reduction of land in Calabar due to past soil erosion range from 55-79% percent with a mean loss of 67%. If accelerated erosion continues unabated, yield reductions by 2020 may be 87%. Soil compaction is a general problem affecting some part of Calabar South especially in the adoption of mechanized agriculture. It has caused yield reduction of 35-60%. It is in the context of these global, economic and environmental impacts of land degradation on productivity in Calabar South that resilience concepts are relevant, since land resources are exhaustible.
The study was done at 45 different farm lands to determine the present state of the soil or land, cause and effect relationship and the soil property that was highly degraded. Different varieties of crops planted at different locations were surveyed and their nutrient status measured. Soil auger was used in the collection of the soil samples between the depth of 0-15cm for shallow and 15-30cm for sub-surface depths respectively. The physico-chemical parameters of the soil analyzed were ph, organic carbon, Nitrogen, Phosphorus, Exchangeable acidity, Cations exchange capacity and base saturation. The equipments listed below in table 1 were used in analyzing soil properties.
SOIL PROPERTIES | EQUIPMENTS FOR MEASUREMENT |
PH | Potentiometer using glass electrode(Bates, 1954) |
Organic Carbon | Oxidation Method (Allison 1965) |
Total Nitrogen | Micro Kse/dahi Method(Bremer and Melvaney, 1982) |
Exchangeable acidity | Titration Method |
Exchangeable Cations | Atomic absorption spectrometer (AAs) |
Cation exchange capacity | Titration using (Chapman, 1965) |
Base saturation | Total exchangeable bases (Ca, Mg, K, Na) divided by their percentages. (Nssc 1995) |
Soil Properties and Equipments
Soil loss and runoff were measured at each study location and their respective cumulative yield calculated from the data obtained at the field. Runoff was calculated using the velocity area technique with the formula;
Q=AV,
where
Q= Discharge
V= Water velocity
A= Cross sectional area of the soil
The result from the research findings is as presented in Table 2 and 3 respectively.
Sampling point | Crop cultivated | Depth (CM) | PH | Organic carbon ( C) % | Nitrogen (N) (kg) | Available phosphorus (p) (kg) | Potassium K (kg) | Cation Exchange Capacity (CEC) ( mol/mg) | Base Saturation (%) |
Water yam | 0-15 15-30 | 4.8 5.7 | 0.49 0.65 | 2.30 3.45 | 3.1 4.5 | 0.18 0.34 | 6.30 8.45 | 76 84 | |
Yam | 0-15 15-30 | 5.8 6.3 | 0.31 0.45 | 2.50 4.20 | 1.5 3.2 | 0.32 0.45 | 5.9 7.20 | 68 82 | |
Cowpea | 0-15 15-30 | 3.9 5.4 | 0.32 0.54 | 4.10 5.00 | 3.3 4.1 | 0.29 0.36 | 4.50 6.30 | 72 81 | |
Melon | 0-15 15-30 | 5.3 6.7 | 0.57 0.49 | 4.90 6.21 | 2.4 3.3 | 0.15 0.21 | 3.20 5.40 | 59 65 | |
Cassava | 0-15 15-30 | 6.8 5.6 | 0.67 0.65 | 6.30 7.23 | 2.7 3.5 | 0.26 0.30 | 6.50 7.35 | 60 78 | |
Water Yam | 0-15 15-30 | 7.2 4.3 | 0.72 0.69 | 2.40 3.50 | 2.9 4.2 | 0.42 0.51 | 5.20 6.50 | 70 89 | |
Cocoa Yam | 0-15 15-30 | 4.3 6.5 | 0.69 0.98 | 3.30 4.40 | 2.1 3.3 | 0.19 0.28 | 7.30 8.20 | 67 76 | |
Maize | 0-15 15-30 | 51 63 | 0.61 0.82 | 3.60 5.50 | 3.4 5.6 | 0.22 0.31 | 5.3 6.00 | 68 72 | |
Rice | 0-15 15-30 | 4.2 51 | 0.69 0.85 | 5.60 6.70 | 2.5 4.4 | 0.22 0.44 | 3.20 5.40 | 59 64 | |
Tomatoes | 0-15 15-30 | 4.0 5.0 | 0.43 0.80 | 3.60 4.80 | 2.8 4.9 | 0.15 0.30 | 4.70 5.80 | 53 69 | |
Pepper | 0-15 15-30 | 3.2 4.9 | 0.43 0.71 | 7.30 9.60 | 2.4 3.8 | 0.26 0.32 | 4.70 6.90 | 58 72 | |
Sweet potatoes | 0-15 15-30 | 4.2 63 | 0.60 0.75 | 6.30 8.50 | 4.2 6.5 | 0.24 0.41 | 5.20 6.50 | 61 74 | |
Waterleaf | 0-15 15-30 | 4.9 7.3 | 0.56 0.70 | 3.32 6.55 | 2.3 4.5 | 0.32 0.41 | 6.20 7.40 | 34 66 | |
Okro | 0-15 15-30 | 2.6 4.5 | 0.49 0.60 | 5.60 7.50 | 3.4 5.6 | 0.32 0.51 | 6.20 7.80 | 59 61 | |
Vegetable | 0-15 15-30 | 6.9 7.5 | 0.39 0.65 | 2.50 4.40 | 2.5 4.7 | 0.27 0.37 | 4.30 6.90 | 57 70 | |
Spinach | 0-15 15-30 | 4.3 5.0 | 0.51 0.63 | 3.20 | 3.9 5.3 | 0.29 0.39 | 5.20 6.50 | 63 71 | |
Bitter leave | 0-15 15-30 | 5.2 6.2 | 0.42 0.54 | 6.70 8.40 | 2.4 4.9 | 0.36 0.46 | 5.40 6.30 | 52 69 | |
Otazi | 0-15 15-30 | 4.9 7.6 | 0.34 0.59 | 2.30 5.40 | 3.5 | 0.24 0.32 | 6.90 8.50 | 63 70 | |
Afang | 0-15 15-30 | 6.5 8.7 | 0.26 0.50 | 2.70 4.70 | 2.4 4.0 | 0.28 0.41 | 6.30 7.40 | 57 72 | |
Etinkene | 0-15 15-30 | 3.5 5.3 | 0.41 0.59 | 4.50 6.50 | 2.4 3.5 | 0.34 0.55 | 4.10 6.20 | 53 65 | |
Garden Egg | 0-15 15-30 | 4.3 6.9 | 0.38 076 | 3.40 4.90 | 2.2 4.5 | 0.19 0.28 | 3.10 4.50 | 44 75 | |
Sugar cane | 0-15 15-30 | 4.6 6.9 | 0.42 0.66 | 5.20 8.20 | 3.1 5.2 | 0.20 0.46 | 3.40 5.60 | 33 49 | |
Scent leave | 0-15 15-30 | 3.4 6.4 | 0.34 0.84 | 3.50 5.40 | 3.4 4.3 | 0.15 0.36 | 3.20 4.50 | 41 62 | |
Curry leave | 0-15 15-30 | 3.2 4.9 | 0.34 0.76 | 4.60 6.40 | 3.3 4.7 | 0.12 0.47 | 6.50 3.20 | 36 54 | |
Ginger | 0-15 15-30 | 0.6 6.8 | 0.42 0.69 | 3.40 7.40 | 2.6 3.4 | 0.24 0.56 | 5.10 3.10 | 43 67 | |
Pineapple | 0-15 15-30 | 3.9 6.7 | 0.34 0.75 | 4.40 5.80 | 2.2 3.8 | 0.18 0.41 | 3.00 4.60 | 34 56 | |
Banana | 0-15 15-30 | 4.9 8.3 | 0.41 0.83 | 6.30 7.80 | 3.4 4.9 | 0.21 0.46 | 3.20 5.30 | 42 54 | |
Groundnut | 0-15 15-30 | 6.3 7.2 | 0.36 0.74 | 3.20 4.80 | 2.3 4.1 | 0.16 0.58 | 2.40 4.10 | 48 73 | |
Lettuce | 0-15 15-30 | 5.2 6.9 | 0.41 0.98 | 2.80 5.90 | 2.0 3.0 | 0.25 0.49 | 2.30 4.70 | 64 78 | |
Melon | 0-15 15-30 | 4.4 5.7 | 0.31 0.52 | 4.10 5.20 | 4.3 5.6 | 0.36 0.74 | 3.40 5.60 | 50 65 |
Soil Physico- Chemical Properties for Different Varieties of Crops Cultivated in Calabar South.
Table 2 depicts that the selected physico-chemical properties of soil varies between the surface layer of (0-15cm) and subsurface of (15-30cm). The research further revealed that due to land degradation, most of the nutrients were leached in to the sub-surface. The resultant effect was that plants restricted to shallow depth did not do well. At certain times some were seen to die because they were no more having nutrients from their roots, this affected their productivity negatively.
The research further revealed that, severe land degradation has affected significant portion of Calabar South’s arable land, decreasing the wealth and economic development of the study area. As land becomes less productive, food security is compromised and competition for dwindling resources increases, the seeds of famine and potential conflict are sown.
Recently in Calabar South, agricultural activities have increased vastly at the expense of natural forests, rangelands, wetlands and even deserts. Some of the expansion is compensated by farmer’s investment in soils, such as fertilization, terracing, and tree planting. New soil formation also occurs through natural processes, but in general these proceed too slowly to compensate for human-induced degradation as shown in Figure 3 below.
Degraded Land Due to Poor Farming Practice in Calabar South
This research is based on consultation with experts, extrapolation from case studies, field experiments and other micro studies or inferences from landuse patterns, current land status, trends, and to what extent the degradation processes are human-induced.
Nutrient depletion as a form of land degradation has a severe economic impact on the study area where it represents a loss of long-run carrying power for farmers and negative externalities for the urban populations. Farmlands used for the cultivation of crops such as Maize, Okro, Water leaf, Pepper, Vegetables, Spinach and Afang had their N.P.K nutrients highly depleted because of their shallow root system which can no longer get nutrient from the leached soil. The economic impact of land degradation is extremely severe in Calabar South. On plot and field scales, erosion can cause yield reductions of 50-70% in some root restrictive shallow lands of Anantigha.
Eni et al, (2010), have estimated nutrient balances for some parts of the study in his findings; he estimated annual depletions of soil fertility at 32kg Nitrogen, 5kg phosphorus and 18kg potassium per hectare of land degraded. In 2002 about 85% of Calabar South farmland had nutrient mining rates at more than 30kg nutrients (NPK)/hectare yearly and 40 percent had rates greater than 60kg/ha yearly. Partly as a consequence, cereal yields are the lowest in the study area, averaging about one tonne per hectare for the same ten years age. Within specific agro-ecological environments, experimental data from the field allow soil degradation processes to be observed with greater precision.
Long term data obtained from the field indicates that intensive farming can cause yield reductions of 60% and more in some parts of Calabar South environments. Even under best variety selections and management practices, yields are stagnated and even fallen under long-term intensive monoculture for irrigated cassava and rain fed corn.
Patterns of degradation vary in Calabar South according to agro-ecological conditions, farming systems, levels of intensification, and resource endowments, but this also interact with social and economic systems. The areas of prime concern for this chapter are the Calabar South marginal lands, which have low physical resilience to land degradation, and are also associated with societies in which property rights are weakly defined, information systems are weak and managerial capacity is low.
Assessing the effects of land degradation in the study area is not an easy task, a wide range of methods were used. Some authors examined the risk of degradation in climatic factors and land use rather than the present state of the land. The methodology utilized for this study is the cause-effect relationship between severity of degradation and productivity. Criteria for designating different classes of land degradation into Low, moderate, high are generally based on soil properties rather than their impact on productivity as presented in figures 4, 5, and 6.
Shows low degraded land
Land degradation in the study area is treated as an open-access resource; it is then difficult to reclaim the value of soil improvements, so land users lack incentives to invest in maintaining long term soil productivity. In areas of low population density, land is abandoned when it has been degraded, and farmers move on to clear new land, leaving the degraded land as a negative externality.
Shows moderate degraded land
Shows high degraded land
Land degradation is a broad term that can be applied differently across wide range scenarios in the study area. The concept of land degradation was considered in four ways which includes, the effect on the soil productivity and the environment around, decline in the land usefulness, loss of bio-diversity, shifting ecological risk and a reduction on the land productive capacity.
Vulnerable lands are exposed to stresses such as accelerated soil erosion by water, soil acidification and the formation of acid sulphate resulting in barren soil, and reduced crop yields. Agricultural activities such as shifting cultivation, without adequate fallow periods, absence of soil conservation measures, fertilizer use and a host of possible problems arising from faulty planning or management of the land all lead to intense land degradation within the study area. Table showing cumulative soil loss and runoff in relation to crop yield in the study area is therefore presented overleaf.
Sampling points | Crops cultivated | Soil loss (Mgh-1) | Runoff (mm) | Cumulative Yield (Mg/ha) |
Farmland 1. | Water melon | 41 | 12 | 10.5 |
2. | Yam | 63 | 18 | 8.3 |
3. | Cowpea | 20 | 6 | 25.6 |
4. | Melon | 35 | 8 | 18.7 |
5. | Cassava | 42 | 16 | 11.4 |
6. | Water yam | 45 | 14 | 10.3 |
7. | Cocoa yam | 43 | 15 | 12.1 |
8. | Maize | 56 | 22 | 10.7 |
9. | Rice | 49 | 20 | 9.6 |
10. | Tomatoes | 7 | 25 | 8.0 |
11. | Pepper | 63 | 46 | 4.5 |
12. | Sweet potatoes | 33 | 15 | 14.7 |
13. | Water leaf | 89 | 48 | 3.2 |
14. | Okro | 60 | 35 | 5.4 |
15. | Vegetable | 52 | 38 | 7.6 |
16. | Spinach | 56 | 32 | 8.9 |
17. | Bitter leaf | 42 | 26 | 10.8 |
18. | Otazi | 53 | 31 | 6.7 |
19. | Afang | 66 | 42 | 4.1 |
20. | Etinkene | 13 | 20 | 9.6 |
21. | Garden Egg | 38 | 17 | 8.5 |
22. | Sugar cane | 42 | 21 | 9.6 |
23. | Scent leave | 45 | 24 | 10.2 |
24. | Curry leave | 37 | 18 | 6.5 |
25. | Ginger | 44 | 20 | 7.9 |
26. | Pineapple | 39 | 22 | 8.6 |
27. | Banana | 41 | 20 | 6.8 |
28. | Groundnut | 34 | 12 | 10.3 |
29. | Lettuce | 31 | 16 | 9.2 |
30. | Melon | 23 | 8 | 5.3 |
Cumulative Soil Loss and Runoff in Relation to Crop Yield in The Calabar South
Table 3 indicates that the greater the soil loss and runoff rates, the smaller the cumulative yield. Farmland number 13, in which water leaf was cultivated had a higher value for soil loss of 89mg/ha and runoff of 48mm, with a lower cumulative yield of 3.2 mg/ha. This means that the soil was severely eroded due to erosion which washed away all the available nutrients. Cowpea located in farmland 3 had the lowest soil loss and runoff rate of 30mg/ha and 6mm respectively with a higher value of 25.6mg/ha for cumulative yield. This was so because the cowpea had a symbiotic relationship with the soil, although it was getting its nutrient from the soil, the plant also played protective role to the soil by serving as a cover crop thereby reducing the runoff rate at the soil surface.
This research have shown that soil erosion carries away a large volume of soil equivalent to one meter deep over 250,000 hectares every year. Some 194 million hectares of land are affected by water erosion. Recently, 6.1 million hectares of land have been lost to water erosion in the study area. Deforestation is also widespread, about 6 million hectares of forest are lost each year. The destruction of the forests is mainly a result of clearance for agriculture. The search for fuel wood, the growing frequency and severity of forest fires, are also taking their toll. As a result of this problem, Crop residues and animal manure, which were previously returned to the soil to add valuable nutrient have to be burnt for fuel.
Land degradation in Calabar south also exhibits hydrological conditions, where vegetal cover is removed, the soil surface is exposed to the impact of raindrops which causes a sealing of the soil surface, and less rain then infiltrates the soil. As runoff increases, stream flow fluctuates more than before, flooding becomes more frequent and extensive, and streams, springs become ephemeral. These conditions encourage erosion; as a result, sediment loads in rivers are increased, dams are filling with silt, hydro-electric schemes are damaged, navigable waterways are being blocked and water quality deteriorates.
Attempts to prevent land degradation in the Calabar South have been unsuccessful. One of the main reasons was that these attempts were centrally organized and it produced few short-term benefits for the farmers who had to execute them. The farmers had little motivation for the hard manual work involved in erecting mechanical barriers to control soil erosion. Government must spear head the formulation of policies, mobilize the people and initiate programs and projects that are needed for sustainability.
The key action required to combat land degradation in Calabar South is to develop a long-term land conservation plan which will provide the necessary continuity of the approach. These long-term plans need to be fashioned to suit the exact requirements of individuals in the study area. They should be based on three key principles; improving land use, obtaining the participation of the land users and developing the necessary institutional support.
However, agricultural policies can have a profound effect on land use. Subsidies, incentives and taxes can all have a big effect on what crops are grown where and whether or not the land is well managed. Governments attempting to achieve self-sufficiency in food crops frequently promotes policies which result in marginal land being misused, this, in turn leads to land degradation. On the other hand, the price of food crops is sometimes controlled and kept to such a low level that it becomes pointless for farmers to manage their crops or land well, this also results in land degradation. All government policies which affect the economics of land use should be carefully reviewed and where necessary, modified so that they encourage productive and sustainable land use rather than destructive practices.
Calabar South government explicitly subsidizes practices that increase land degradation, and tax activities that tend to reduce degradation. Examples are subsidies on cultivation of upland crops that drive expansion into the marginal lands; subsidies on water and energy in irrigation schemes; tariff protection for land-degrading crops, and export taxes on more environmentally benign crops. Reversal of these policies will have very high benefit-cost ratio, since their net cost is low, zero or even negative as long as political costs are disregarded. Increased intensity of cultivation in ecologically fragile upper water shed areas of Calabar have contributed to land expansion. Developing countries in particular have undertaken extensive reform of trade policies in manufacturing sectors, driven both by unilateral goals and by the need to conform with international obligations as signatories to regional and multilateral trade agreements.
Agricultural trade reform has lagged behind this process, with the result that average agricultural tariffs are now equal to or greater than those on non-agricultural goods in developing countries such as Nigeria and specifically in Calabar South(Anderson, 2006). Equilibrium simulation experiments, aimed at implementation of package of trade liberalization measures in Calabar South including a modest reduction in cereals prices, was found to exert a substantial effect on land use. The price of cassava the major annual crop grown in Calabar South falls in these experiments by about 0.75 percents. This fall, along with rises in wages and some input prices, causes a contraction of about 0.4 percent in demand for upland land for seasonal crops. If cassava land is primarily responsible for erosion, from upland fields and the base, annual soil loss from the upland farm will be 65-75 million trillion/year, the trade reforms permanent ground cover is re-established assuming that this is what happens after cassava production cases.
Research valued the nutrients lost to soil erosion in Calabar South at 30million/ton, adopting that as a very conservative indicator of the total value of soil lost, the experiment yields a direct, on-site gain of roughly 150million in addition to the other benefits that the trade liberalization brings to the economy. In these and similar tropical economies, substantive trade liberalization will result in major land use changes. Relaxing protectionist policies on crops which contribute to land degradation in Calabar south will shift their production to countries and environments where they can be grown at lower environmental cost.
In the case of subsidies, their relaxation creates fiscal savings that provide an opportunity to compensate farmers, who are often extremely poor. For environmental taxes, e.g. on activities that lead to downstream siltation, the challenge is to monitor and assess such widespread activities. Addressing policy-induced distortions that operate through markets to promote land-degrading activities is the most efficient single means to address land degradation in Calabar South.
The success of policy reforms, however, relies on the pervasiveness of markets and the feasibility of market-based instruments. Not as trade policy reforms on their own but a panacea for environmental damage, with comparative advantage in land degrading crops, greater trade openness without complementary environmental protection policies may lead to rapid worsening of land degradation.
Finally the calabar south government had tried to set-aside programs, land use zoning policies and establishment of conservation areas, bans on degrading activities and public reforestation projects. Cross River State afforestation projects, is targeted at increasing forested areas in Calabar South by 50% and 15% decrease in cultural areas. The current program, however, lacks “Volunteerism” in participation, and therefore suffers from low cost effectiveness and high cost of performance monitoring and evaluation. In general, it is very difficult and costly to police and enforce bans against common and widely dispersed practices when these practices are profitable to land users or perhaps even necessary for survival. Project-based payment for environmental services schemes introduced in Calabar South is meant to provide a means of paying compensation to farmers who desist from environmentally undesirable activities. But since there is no internal mechanism for decreasing cost replication of payment for environmental services measures, in benefit cost terms these are expensive interventions if they are to be widely applied even before counting the cost of contract enforcement and monitoring.
Over the years, there has been a progressive change in the approach to agricultural practices from crop substitution to integrated farming system. A concern for environmental aspects has been explicit till many other projects came up. However, the way this was undertaken, and the priority given to conservation, differs greatly. The use of erosion control structures such as Bench terraces, contour bank, contour ditches were localized.
Recently, based on research findingds, Calabar South farmers started using erosion control measures devoid of physical structures. This marked a major departure from the previous approach. The objective was not simply soil conservation, but sustainable farming systems. Among the key lessons learned are:
The importance of having a master plan for water shed development.
The importance of the local people participating in all levels of conservation.
The use of vegetable barriers as the most pertinent and cost effective erosion control measures in the area.
The introduction of new technology in controlling land degradation was made use of in Calabar. These have led to a higher and more assured crop yield while controlling soil erosion.
The introduction of a mixture of leguminous creepers as cover crops on land that is planted with rubber and oil palms. Research has shown that desmodium ovalifolium, stylosanthes gracillis and clitoria ternetea provides useful ground cover, and help to control land degradation.
The provision of improve varieties and a large increase in the use of fertilizer encourages high yield and provide good ground cover.
That the recommendations should be exceptionally comprehensive and user friendly.
Finally, the farming system utilized must correctly identify a wide range of indicators and avoid the usual problem of selection of a limited number that can only be applied to specific situations
There are six major causes of land degradation in Calabar South, they include; deforestation, shortage of land due to increased populations, poor land use, insecure land tenure, inappropriate land management practices and poverty, problems of valuation, and even of assigning causality, make it impossible to compute accurate benefit-cost ratios for reducing land degradation. A precautionary approach, must take into account the relative magnitude of the problem, the relative importance of land degradation to the poor and the relative weakness of existing institutional and market-based mechanisms to deal with on-site degradation and externalities this means that efforts to reduce land degradation should focus on sloping lands and forest margin areas in Calabar South and should depend mainly on market-based instruments, accompanied by efforts to ease and increase investment in the development of technologies for sustainable agriculture
Land resources are non renewable and it is necessary to adopt a positive approach to ensure sustainable management of these finite resources. Soil scientists have an obligation not only to show the spatial distribution of stressed systems but also to provide reasonable estimates of their rates of degradation. Many assessments in Calabar South have dealt with land degradation risks rather than dealing with degradation status, its socio-economic cause and its political driving force. Most estimates of soil erosion for instance, have been on erosion hazard not actual observed erosion. There are consequently large differences between estimates of areas at risk and areas actually affected by land degradation
One of the most obvious direct causes and driving forces of land degradation in Calabar South is the mismatch between land potential and actual land use which is different from land cover and it includes information on land management and inputs. Some socio-economic data have to be collected at farm level during rapid rural appraisal or other livelihood surveys to establish the general conditions leading to certain land use practices. It is important to realize that the socio-economic parameters collected should be simplified and classified according to their role in the assessment of land degradation.
This research can be summarized in two points. Firstly, it was observed that land degradation is proportionally and absolutely very severe in Calabar South, where it represents a loss of long-run earning power for farmers and negative externalities for larger rural populations. Monetary values aside, the problem of land degradation becomes more acute when the welfare of the poor is given higher priority. Secondly, we must note that the same policy instruments that we have advanced as the best means to alleviate land degradation are also components of reform packages with much broader economic development aims. In this sense our land degradation proposals are “bundled with” measures that deliver gains that extend well beyond the environment.
Modern computers and electronics, such as smartphones and supercomputers, have been developed in accordance with Moore’s law [1], which implies improvement in cost, speed, and power consumption by scaling down devices. However, the fundamental physical limits and increased fabrication costs pose a hindrance to sustainable development of computing technology [2, 3]. Moreover, with the advent of the big data era, unstructured data and data complexity explosively increases, imposing constraints on the conventional computing technology owing to the von Neumann bottleneck [4, 5]. Neuromorphic systems [6, 7], which mimic the nervous system in the brain, have recently become known as strong candidates to overcome these technical and economic limitations owing to their proficiency in cognitive and data-intensive tasks, together with their low power consumption. To successfully implement these neuromorphic systems, it is of utmost importance to research and develop artificial synapses capable of synapse functions, high reliability, low energy consumption, etc. [8, 9]. In the plethora of possible devices, memristors have gained the spotlight because of their desirable characteristics as artificial synapses [10, 11, 12], including device speed [13], footprint [14], low energy consumption [15], and analog switching [16, 17].
In this chapter, we introduce the basic concepts of neuromorphic systems and memristor synapses. We also describe diverse examples for state-of-the-art artificial synapses in terms of novel functional materials and device architecture. We then briefly review the implemented neuromorphic systems based on memristor synapses.
Conventional computing architecture, that is, von Neumann architecture, forms the groundwork for modern computing technologies [3, 18]. Despite tremendous growth in computing performance, classical architecture currently suffers from the von Neumann bottleneck, which results from data movements between the processor and the memory unit [4, 5]. The memory wall issue, causing high power consumption and low speed, hinders the continuous development of computing technologies [4, 5, 9]. Moreover, artificial neural network (ANN) algorithms, such as deep learning [19], deal with image classification [20, 21], sound recognition [22, 23], specific complex tasks (e.g., the AlphaGo [24]) and so on. Although the ANN algorithms have exhibited superior performance over the conventional computing technologies, they are, at present, constructed on the von Neumann architecture; hence, considerable time and energy resources are required for their operation [8, 9]. Neuromorphic architecture [6, 7], a bio-inspired computing architecture, is one of the most promising candidates to resolve these problems. The neuromorphic systems take advantage of the cerebral nervous system, which consists of a massive parallel connectivity between the neurons (i.e., processor) and the synapses (i.e., memory), indicating the absence of the von Neumann bottleneck [8, 9]. Figure 1 shows the shift of the computing architecture from von Neumann architecture (Figure 1a) to neuromorphic architecture (Figure 1b). The von Neumann architecture shows that the processor and memory are separate, leading to the von Neumann bottleneck. In contrast, in the case of neuromorphic architecture, the neurons and synapses are combined, alleviating the bottleneck issue. The neurons are uncomplicated computing units, the synapses are local memory units, and the communication channels (red line) connect numerous neurons and synapses. It should be noted that the practical purpose of neuromorphic systems is not to replace the von Neumann architecture completely, but to supplement the conventional architecture to make up its leeway, especially for intelligent tasks such as image recognition and natural language processing.
(a) Conventional computing architecture (von Neumann architecture). Data transfer is performed through the bus (memory wall). (b) Neuromorphic architecture. In contrast to von Neumann architecture, von Neumann bottleneck does not exist.
Memristors that consist of a storage layer inserted between the top and bottom electrodes can undergo dynamic reconfiguration within the storage layer with the application of electrical stimuli, resulting in resistance modulation referred to as memory effect [16, 17]. The changed resistance state can be retained even after electrical inputs are removed, and memristors are based on the history of applied electrical stimuli. These capabilities lead to analog switching, which resembles biological synapses where the strength (or synaptic weight) can increase or decrease depending on the applied action potential [25, 26]. When neuromorphic architecture is implemented on the conventional computing architecture, the synaptic weights are stored in the memory unit and are continuously read into the processor unit to transfer information to post-neurons. In other words, practically, the von Neumann bottleneck still remains challenged. However, in case of memristor synapse-based neuromorphic systems, the synapses can not only store a specific weight but also naturally transmit information into post-neurons, overcoming the von Neumann bottleneck and improving system efficiency [8, 9]. In addition to analog switching, memristors have exhibited desirable device properties, including nanoscale footprint [14], long endurance and retention [17, 27], nanosecond switching speed [13, 15], and low power consumption [15]. Owing to these characteristics, memristors have emerged as promising candidates for artificial synapses. However, it should be noted that no specific material/device system has shown all-encompassed characteristics so far.
Depending on their storage layer and electrode, memristors can be broadly classified into two categories: cation-based devices and anion-based devices. It is widely believed that cation-based devices are based on migration of metallic cations (see Figure 2a) [17, 28]. They employ electrochemically active materials such as Ag or Cu as an electrode [29, 30, 31, 32]. The counter electrode is usually an electrochemically inert material, such as Pt, Au, or W, and the storage layer consists of a solid-electrolyte like Ta2O5, SiO2, or Cu2S. For example, when a positive voltage is applied to an Ag top electrode, the atoms from this electrode are electrochemically oxidized to Ag+ cations because of anodic reaction, which are then dissolved into a solid-electrolyte layer. The Ag+ cations migrate across the solid-electrolyte layer toward the counter electrode (e.g., Pt) depending on electric field. At the Pt electrode, the Ag+ cations are electrochemically reduced to Ag atoms because of cathodic reaction and are deposited on its surface. Thus, conductive filaments grow toward the Ag top electrode, and eventually the filaments bridge the anode and the cathode, indicating that the device switches into ON state (low resistance state) as shown in Figure 2a. In contrast, when a negative voltage is applied to the Ag top electrode, the Ag filament begins to dissolve anodically, starting from the interface of the Ag top electrode/Ag filament, which results in OFF state (high resistance state). Owing to this process, cation-based devices are referred to as electrochemical metallization memories and conductive bridging random access memories. It should be noted that the initial formation of conductive filaments is called the electroforming process, which needs a voltage higher than a switching voltage.
(a) Cation-based devices: Through electrochemical reaction, metal cations M+ migrate toward the counter electrode and form conductive filaments between the top and bottom electrodes. (b) Anion-based devices: During electroforming, the soft-breakdown leads to O2− ions (oxygen vacancies V), and the oxygen vacancies form conductive filaments between the top and bottom electrodes.
Anion-based devices usually require the initial electroforming process and are switched depending on the O2− anions (or positively charged oxygen vacancy V) induced into the storage layer by soft-breakdown (see Figure 2b). These devices consist of a sub-stoichiometric storage layer made of HfOx [33, 34], TaOx [35, 36], WOx [37, 38], etc. When a positive forming voltage is applied to the top electrode, the induced O2− ions migrate toward it. This anion motion causes a change in the valence state of the cation to keep the charge neutral; hence, these devices are also referred to as valance change memories. Throughout the process, the oxygen vacancies continue to form conductive filaments in the storage layer. When the filaments bridge the top and bottom electrodes, current flows through the filaments, with the result that the device switches to ON state. Contrastingly, when a negative voltage is applied to the top electrode, the O2− ions either recombine with oxygen vacancies present in the filaments or oxidize the cation precipitates, with the result that the device switches to OFF state. Thus, memristors could be understood to some extent based on cation- and anion-based mechanisms. However, identifying the precise mechanism of a specific device is a challenge because of the presence of mingled mechanisms and different driving forces or locations. Therefore, further studies are necessary for a deeper understanding of the switching mechanism.
Various properties of memristor synapses that affect the performance of neuromorphic computing need to be discussed in detail. Among them, representative characteristics such as the linearity in weight update, multilevel states, dynamic range (ON/OFF ratio), variation, retention, endurance, and footprint will be addressed in this section as they can substantially affect computing achievements [8, 35]. The linearity of the weight update indicates the linear relationship between synaptic weight change (∆w) and programming pulse. In other words, the conductance of the memristor synapse changes linearly in accordance with the number of programming pulses, which is associated with the mapping of weight in the algorithms for conductance in memristor synapses. Hence, the linearity of weight update affects the performance (e.g., accuracy). Notably, most memristor synapses show a nonlinear weight update, where the conductance change gradually saturates, as shown in Figure 3. Hence, the nonlinearity of weight update should be improved to achieve highly efficient computing.
(a, b) Nonlinearity of weight update. Current abruptly changes in initial pulses and gradually saturates. Most memristors exhibit a nonlinear relationship. All figures are reproduced with permission from Ref [39, 10], respectively. Copyright (2017, 2010) American Chemical Society.
The resolution capability of storage is influenced by multilevel states and dynamic ranges because numerous conductance states can distinguishably store individual pixels of input patterns. Moreover, variations, including cycle-to-cycle and device-to-device variations, could degrade neuromorphic computing, particularly in large-scale systems. However, considering that neuromorphic computing exhibits the fault-tolerant property, neuromorphic architectures could be immune to the variation to some extent, and this is supported by several papers [8, 35, 52]. In addition, memristor synapses are repeatedly updated during the training process and should retain the trained weights (i.e., final conductance). Subsequently, the larger the endurance cycles and retention time, the better are the achievements of the neuromorphic network. Last but not least, it is desirable that device’s footprint is below sub-10 nm because high density leads to more synaptic devices that store learned information under a specific area [8].
Furthermore, it is efficient to improve the characteristics of memristor synapses depending on individual neuromorphic networks, because a desirable memristor synapse capable of being employed into neuromorphic systems is yet to be reported. Supervised learning-based networks [35, 40, 41, 42, 43, 44], for example, are less vulnerable to cycle-to-cycle and device-to-device variations. This is because memristor synapses are updated according to calculated errors under known target values. By contrast, the networks based on unsupervised learning [39, 45, 46, 47] are directly affected by the variation owing to unknown target values. Therefore, memristor synapses need to be designed or selected depending on individual neuromorphic networks.
Memristors for synaptic devices with two-terminal (e.g., vertical/planar-type and gap-type) and three-terminal (e.g., field-effect transistor and lateral coupling type) structures are manufactured by well-established processing technologies [7, 8, 9, 10, 11, 12, 35, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55].
In the case of a two-terminal structure, when different voltages are applied to each of the two electrodes, resulting in current flow through the insulator, varying the conductance of the device enables emulation of biological synapse functions such as synapse plasticity [10, 11, 12, 16, 35, 48]. In particular, the crossbar array of two-terminal devices has received attention because of its characteristics relevant to synaptic devices, such as scalability for high density, simple fabrication process, low cost of fabrication, parallel connection structure, low power, fault-tolerance, and compactness. Thus, they are expected to provide an appropriate structure to support synaptic electronics. The type of two-terminal memristors that are being reffered to as the artificial synapses includes resistive random-access memory, phase change memory, conductive bridge memory, and spin-based memory. Although two-terminal devices are attracting much attention because of their ease of implementation of crossbar arrays, a two-terminal device, as a matter of fact, requires a select device to eliminate the sneak path that occurs in a crossbar array configuration. Additionally, it is difficult to imitate complex synaptic functions such as hetero-synaptic plasticity (e.g., modulatory input-dependent plasticity).
Three-terminal structures (e.g., field effect transistor memory and floating/gate transistor memory) with tunable conductance of channels between the source and the drain are also considered as synaptic devices [49, 50, 51]. The gate electrode acts as the pre-synapse, transferring the stimulus to the insulating layer, indicating the cleft of the synapse, and modulates the conductance of the channel representing the synaptic strength. Although the three-terminal structure is more complicated than the two-terminal structure and is disadvantageous in terms of density, the terminal for the signal transmission process and the learning terminal are separated such that simultaneous signal processing is possible, and complex synapse functions such as hetero-synaptic plasticity can be mimicked. Moreover, they do not require an additional selector device to reduce sneak current in an integrated array architecture.
Recently, going beyond simply implementing a synapse function, researchers have demonstrated advanced concepts of synapse device functions, including self-rectification, photo-assisted synaptic plasticity and neuromodulation to achieve more delicate imitation of the human brain and learning-and energy-efficiency in neurocomputing.
In [35], Choi et al. fabricated a self-rectifying memristor synapse through a two-terminal structure (Pt/TaOy/nanoporous TaOx/Ta), which is capable of suppressing unwanted leakage pathways and then a 16 x 16 crossbar array using only the devices without an additional selector (see Figure 4a and b). The mechanism of memristive switching and synaptic functions, including long-term potentiation (LTP), STDP (spike-timing dependent plasticity), and long-term depression (LTD) were caused by the migration of O2− ions with oxygen vacancies V by applied electric field in the TaOx. In addition, the asymmetric interface contacts of Pt/TaOy and TaOx/Ta prevent the undesired signal by performing the self-rectification function without the selector.
(a) Schematic of a self-rectifying memristor with a Pt/TaOy/nanoporous TaOx/Ta and cross-sectional image of a memristor synapse. (b) I-V curves of the self-rectifying memristor synapse. (a, b) are reproduced with permission from Ref [35] under a Creative Commons Attribution 4.0 International License. (c) Schematics of the suggested mechanism of how a conductive switching filament is formed by the iodine vacancy migration in the presence of light. (d) Synaptic potentiation and depression behavior of the OHP-based synaptic device. (c and d) are reproduced with permission from Ref [52]. Copyright (2018) John Wiley and Sons.
In [51], Huh et al. reported a synapse device that performs the neuromodulator function of a barristor structure using 2D material as shown in Figure 4c. The three-terminal device consisted of a vertically integrated monolithic tungsten oxide memristor, and a variable-barrier tungsten selenide/graphene Schottky diode, termed as a “synaptic barrister.” This synaptic barristor could implement fundamental synaptic functions, including short-term plasticity (STP), paired pulse facilitation (PPF), LTP, and LTD, with external gate controllability, termed as a neuromodulator in bio-synapse. This architecture potentially offers considerable power-saving benefits while significantly tuning the synaptic weights and intrinsically modifying the synaptic plasticity, in comparison with conventional two-neuronal-based synaptic architectures.
In [52], Ham et al. fabricated an organo-lead halide perovskite (OHP)-based photonic synapse in which the synaptic plasticity is modified by both electrical pulses and light illumination. The switching mechanism originates from the presence of a conductive filament by iodine-vacancy mediator, with its switching states controlled by electric-field domination (see Figure 4d). Using diverse electrical stimuli and relative timing between the input pulses, essential synaptic functionalities such as STP, LTP, and LTD were successfully demonstrated. In addition, owing to the accelerated migration of the iodine vacancy inherently existing in the coated OHP film under light illumination, the OHP synaptic device exhibits light-tunable synaptic functionalities with very low programming inputs (≈0.1 V) as shown in Figure 4d. The ability of high-order tuning of the photo-assisted synaptic plasticity in an artificial synapse can offer significant improvements in the processing time, low-power recognition, and learning capability in a neuro-inspired computing system (Figure 4e).
In [12], Wang et al. designed a diffusive memristor for STP synapses and threshold neurons. The devices contain a switching layer doped with Ag nanoclusters (MgOx:Ag, SiOxNy:Ag, and HfOx:Ag) using the co-sputtering method. The switching mechanism is based on the growth and relaxation of Ag nanoclusters depending on whether the voltage pulse is applied, which was experimentally verified by in-situ high-resolution transmission electron microscopy (HRTEM). The designed device mimicked STP under PPF and PPD. Moreover, the device was used as a threshold neuron along with drift memristor synapse based on TaOx to emulate STDP learning rule. Because the conductance of the device gradually increases according to applied voltage and then abruptly decreases under no applied voltage, the device can be used as a threshold neuron. The results give a potential application for simple artificial neurons as compared with CMOS artificial neurons [53, 54].
Prezioso et al. experimentally demonstrated neuromorphic networks based on memristor synapses (see [55]). In their paper, Al2O3/TiO2−x memristor was used to fabricate a 12 × 12 crossbar array to implement a single-layer network [56]. The single-layer network architecture was schematically described as shown in Figure 5a, where 10 input neurons and 3 output neurons are fully linked by 10 × 3 = 30 synaptic weights (Wi,j). Notably, this ANN architecture naturally corresponds to a crossbar array [9, 35]. Input voltages (Vi = 1…9) assigned from pixels of the 3 × 3 input images (see Figure 5b) were applied to each input neuron. After being applied into the network, the input voltages were individually weighted depending on each synaptic weight. Note that V10 is a bias voltage to control the degree of activation of the output neurons. The output neurons received each weighted voltage through linked weights and then integrated the weighted voltages (∑Wi,jVj), where j and i represent the input (j = 1–9) and output (i = 1–3) neurons respectively. The output neurons converted each integrated voltage into output (fi) ranging from −1 to 1 according to the nonlinear activation function: fi = tanh(βIi), where β adjusts the nonlinearity of the activation function and Ii = ∑Wi,jVj. The activation function can be considered as the threshold firing function in a biological neuron. The synaptic weights were represented by a pair of adjacent memristors (Wi,j = Gi,j+ − Gi,j−) for the effectiveness of weight update. The number of selected memristor synapses in 12 × 12 array were 30 × 2 = 60, due to a pair of memristors (Figure 5c). When the network was under the training process, as shown in Figure 5d and e, memristor synapses between input and output neurons were updated based on the Manhattan update rule, which is classified as supervised learning: ∆Wi,j = ηsgn∑[(ti(n) − fi(n)) × df/dI × Vj(n)], where η is the learning rate, ti(n) is the target value, fi(n) is the output value, and n is the nth input image. After the training process was complete, the memristor synapses retained their final conductance, and the test process was performed without weight update (see Figure 5d). From the test process, the neuromorphic network exhibited perfect classification for the first time in 21 epochs (note that one epoch indicates one training process). Although simple and few input images were used to train/test the neuromorphic network, this work greatly contributed to neuromorphic systems based on memristor synapses in terms of experimental demonstration using crossbar arrays.
(a) Input voltages corresponding to an input image (Vi = 1…9) and a bias voltage (V10). These voltages are fed into the single-layer network where 10 input neurons and 3 output neurons are linked by synaptic weights. (b) The “z,” “v,” and “n” input images. Aside from ideal images, other images contain one noise pixel. (c) The schematic of implemented 10 × 6 crossbar array, a pair of adjacent memristors provide one synaptic weight. (d) When an image (e.g., “z”) is fed into network, pixels for black give VR (read voltage) to the network, otherwise, −VR is applied into the network. (e) An instance of weight update according to Manhattan update rule. The synaptic weights corresponding to sign + should be increased, so that the memristors representing G1,1+, G1,2+, G1,5+, G1,6+, and G1,9+ are applied by set voltage. All figures are reproduced with permission from Ref [55]. Copyright (2015) Springer Nature.
It should be noted that the circuit that acquires sgn[ fi(n)] = sgn[∑Wi,jVj] = sgn[∑(Gi,j+ − Gi,j)Vj] could be implemented by a virtual ground circuit and a differential amplifier [43, 57]. Then, the output value is compared with the target value by circuits using a comparator. According to calculated ∆Wi,j, programming memristors of the array, for example, could be performed as shown in Figure 6 [39]. The test board contains four digital-to-analog converters (DACs) providing voltage pulses through the DACs. The DACs 1–4 represent the chosen bottom line, the unchosen bottom line, the chosen top line, and the unchosen top line, respectively. Using matrix switches (Switch 1 and 2), individual memristor is assigned to the corresponding DAC. The multiplexer (MUX) is operated to obtain currents that flow through memristors in the array by delivering the currents into the analog-to-digital converter (ADC). The ADC obtains the applied voltage of the resistor (1 kΩ), and the voltage is changed into the current. The arrows of Figure 6 represent the current flowing through a chosen memristor in case of write, erase, and read processes. Notably, there are non-idealities such as sneak currents and wire resistance in array-level, which could degrade the performance of neuromorphic computing [35, 44, 58, 59, 60]. The sneak currents affect learning accuracy and epochs because of undesired information, especially large-scale array. In Figure 6, in order to avoid sneak currents during read process, unchosen rows and columns are grounded [39]. Moreover, wire resistance consumes input voltages, so that memristors far from points of input voltage could be applied by smaller voltage than input voltage. This influences output currents, leading to degradation of learning performance. The non-idealities in array-level could be overcome by device functions [35, 44], operational scheme [39, 58, 59, 60], or learning algorithms [35, 40, 41, 42, 43, 44] to some degree.
Circuit scheme for write, erase, and read processes. The figure is reproduced with permission from Ref [39]. Copyright (2017) American Chemical Society.
Neuromorphic systems are one of the most promising candidates to deal with the von Neumann bottleneck caused by the memory wall between memory and process units. Using memristor synapses simply classified into cation- and anion-based devices can resolve this bottleneck owing to their storage and transmittance capabilities. To obtain higher performance of neuromorphic systems, representative characteristics, including the linearity of weight update, large multilevel states and dynamic range (ON/OFF ratio), variation and endurance, and retention need to be improved. In this context, different memristor synapses based on novel materials and device structures were introduced. Finally, we have briefly explained neuromorphic networks based on crossbar arrays of memristor synapses, and the network demonstrated perfect classification after 21 epochs. We believe that this chapter offers a deep understanding of the field of memristor synapses.
This work was supported by the National Research Foundation of Korea (NRF-2016R1C1B2007330 and NRF-2019R1A2C2003704), KU-KIST Research Fund, Samsung Electronics, and a Korea University Future Research Grant.
The authors declare no competing interests.
Authors are listed below with their open access chapters linked via author name:
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