Lupine Publishers | Instructions for Irrigating and Watering Plants (Indonesian Version)

   Lupine Publishers |Agriculture Open Access Journal

Opinion

Written by Ir. Sri Najiyati & Ir. Danarti, human efforts to fulfill and regulate the need for plants for water, which is often called irrigation, have developed since ancient times. Although at that time the methods and tools used were still traditional and makeshift. At present the business has increased with technology. Various characteristics of plants in relation to water have been studied and sophisticated and modern mechanization tools have also been found, so that irrigation can be carried out appropriately both time and method as well as the amount of water requirements without requiring much labor. The description in this book begins with a description of the properties of water in nature and in relation to plants and the characteristics of each type of plant for water needs. Also contains instructions on ways to provide water for plants both traditional and modern, because according to the authors traditional methods are still relevant while modern methods are feasible to be implemented in Indonesia. Water is one of the factors that is very important for plant life. It is not surprising that its existence is very influential on the types of plants that live somewhere. In areas that have high rainfall, we will find plants that need a lot of water, while in areas with low rainfall we will find plants that are resistant to drought.

The role of water for plant life is water as a nutrient solvent in the soil so that plants can easily take the nutrient through the roots as food and at the same time transport the hope to parts of plants that need it. Water is one of the important components in photosynthesis, namely the process of forming carbohydrates from water and carbon dioxide with the help of sunlight. Almost all plant physiology processes including chemical reactions take place in the presence of water. Inside the water plant functions to maintain the firmness of the plant. If the plant lacks water, the plant will wither and then die. Water as a temperature controller in plants when the sun is hot. When the sun is hot, leaves and other parts of the plant will overheat so that the temperature can rise continuously if there is no one to control it, namely water. Water controls the temperature of the plant by evaporation through the stoma on the leaf surface. Because evaporation requires heat, so the temperature of the plant that was too high became constant again. The benefits of water for plants are very large and their presence around the plants is an absolute requirement for plants to take the water for their lives. But its existence can also be a disaster if the amount is excessive. All plants need water, but their needs vary depending on the type. Rice, for example, requires a lot of water almost during its growth period. Instead the cactus will languish if it lives in an environment that contains lots of water. On this earth there are approximately 1.3-1.4 billion cubic km of water. But this large amount is not all around the plants we cultivate but most of them are in the sea, in rivers, in lakes, in swamps, in the air as clouds or as groundwater that is not accessible to the roots of plants. For plants to meet their water needs, cultivating human intervention is very necessary. Human intervention to meet the needs of plants for water is called irrigation. According to the method of administration the irrigation system is divided into three, namely watering systems, aboveground irrigation and subsurface irrigation. Watering is a system of giving water by spraying so that the fall of water to the surface of the soil / plant will be in the form of granules that resemble rain. While irrigation is a system of giving water by flowing and flooding above the soil surface or from below the soil surface. In the past, when humans were not familiar with advanced technology, the new irrigation system was carried out simply with a makeshift tool. The water channels that are made are generally not permanent and are easily damaged so that plants that require a lot of water such as rice can generally only be planted in the rainy season or once a year. Irrigation like this is known as non-technical irrigation. At this time when humans have known advanced technology, irrigation and watering systems have evolved along with technological developments.

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Lupine Publishers | Evaluating the Impacts of Development on Agricultural Land

  Lupine Publishers |Agriculture Open Access Journal

Mini Review

In developing nations like the Kurdistan region in Iraq, experience shows that, during the initial phases of development, urban expansion becomes a top priority and as a consequence agricultural land is often considered as a set aside for forthcoming urban expansion. Hence, agricultural lands are at risk due to the loss of land to urbanization. The Kurdistan region in Iraq is highly suited for agriculture as it boasts significant areas of arable land, fertile soil and various micro-climatic zones [1,2]. Potentially, agriculture in the Region could become an important public revenue and the rehabilitation of this sector translates into reviving village life, creating more job opportunities, encouraging new industries, and upgrading the standard of living and quality of life [3,4]. Agriculture in the region is characterized by its low productivity due to a number of reasons which include using outdated farming practices, not matching agricultural produce with the best fitting environmental conditions. An added factor can also be the loss of prime productive land to urbanization. This note assesses the influences of urban expansion, on wasting arable land and the ultimate consequence on sustainable agricultural production in the Kurdistan region, Iraq.

A practical way forward for increasing agricultural productivity is through land capability and suitability mapping, which spatially and temporally summarizes the extent to which the inherent physical capacity of the land and the associated favorable climatic conditions in a particular area is agricultural production without reducing the soil’s long-term productivity, subject to good management Dent and Young, 1981; Emery, 1986. Hence, land capability and suitability classification are a specific grouping of soils made primarily for agricultural purposes.

The suitability of land for plant production in Iraq was developed based on the Soil Survey Geographic Database (SSURGO) which rates soils based on their ability to support cultivation and farming of common crops without deterioration of the soil over long periods of time. It contained 8 classes ranging from the soils with the potential for agricultural production (class I) to areas not capable of agricultural production such as rock outcrops and sandy heath (class VIII). Under good management, soils in classes from I-IV are capable of producing common cultivated field crops, pasture plants, range plants, and forest trees without reducing the soils long-term capacity. Using the suitability of land for plant productions for Iraq, a new GIS based Land capacity and suitability map was produced for the Erbil governorate and provinces. This binary map was developed based on clustering the 8 classes into two classes. The first soil classes ranked from 1-4 are considered as “Suitable”, while the other classes were considered “Less Suitable” (Figure 1). The map shows that the main urban centers are located on the most suitable land for agriculture as the community is agricultural in nature.

The outcome was then correlated with recently developed Master plans for Erbil governorate and provinces. Using this information, a GIS based map was produced (Figure 2). The results disclosed the estimated loss in productive agricultural land when the current Master plans are implemented, making the estimated loss in Erbil governorates to be around 1514Km². Therefore, under current practice, urban expansion can only be implemented through wasting valuable productive agricultural land. Clearly, the paradox is that as the population grows, the need for urban expansion grows, and the latter can only be carried out by wasting productive agricultural land and endangering sustainable agriculture. This suggests the need revise to the Master plan and to find a healthy balance between development and sufficient food production for the current as well as the growing population. Furthermore, the outcomes can provide the necessary information on the broad agricultural products most physically suited to an area, that is, the uses with the best match between the physical requirements of the use and the physical qualities of the land. Consequently, it can provide guidance on the inputs and planning requirements associated with different management schemes for increasing agricultural production within the region.

Figure 1: The spatial distribution of suitable land for agriculture in Erbil Governorate in Kurdistan Region, Iraq.

Figure 2: The impacts of Master plans on squandering land in Erbil.

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Lupine Publishers | The Newest Agricultural Technologies

 Lupine Publishers |Agriculture Open Access Journal

Abstract

The primary objective of agricultural production is to provide an economical, sustainable and productive industry in plant and animal production. For this purpose, alternative solutions are provided to the problems that need solution or improvement and to facilitate agriculture in various areas such as increasing productivity and product quality, minimum input usage, food reliability, protection of natural resources and environment in agricultural production. In this study, the technologies which are successfully applied in plant production and animal breeding were addressed by taking into consideration the advances made especially in recent years.

Keywords: Precision agriculture; Smart farming; Precision livestock farming; Autonomous tractor; Unmanned aerial vehicles

Introduction

The agricultural sector has been adversely affected by global market instabilities, economic crisis, animal diseases and climate changes in recent years. In addition, structural problems such as the average size of farms not allowing adequate investments to increase productivity, absence of large-piece agricultural lands, lack of education, agricultural employment and population growth as well as the emergence of alternative uses of agricultural products such as biofuels cause inefficiencies [1]. Due to the rapid increase in the world population and urbanization, agricultural land per capita and natural resources such as water are decreasing due to the decrease in agricultural areas. For this reason, it has become necessary to increase productivity in agricultural production through technological and genetic methods. Excessive use of chemicals and fertilizers, during the intensive agricultural practices made to increase efficiency, has caused problems such as environmental pollution in soil and ground water and the loss of the production power of the field over time. Today, increasing product quality, minimum input usage, food reliability, protection of natural resources, increased environmental awareness, economic production and sustainable agriculture concepts have become a priority, despite the previous goals of increased yield and productivity.

As a result of the rapid developments in information technology following the mechanization, automation, and control technologies during the development period of agricultural production, today, intelligent machines and production systems that control machines have begun to take over traditional production methods. Information technology consists of hardware, algorithms and software developed for the management of the collection, processing, storage, transfer and use of information processes. The implementation of present knowledge and experiences in agriculture together with the machine learning, deep learning, artificial intelligence, modeling and simulation applications enabled the development of real-time and automated expert systems, autonomous tractors or agricultural machines and agricultural robotics applications.

Precision Agriculture

Precision agriculture technologies, combining with control, electronics, computer and data base with the account data, present an advanced system approach. Using global positioning system, geographic information system, variable rate application and remote sensing technologies, precision agriculture technologies, contrary to common fixed-level application methods which are applied at all same to whole land, use the variable-level application methods (based on application of fertilizer and chemicals to each section to its own needs, tillage at different levels, planting at different norms, irrigation and drainage at different levels) determining land and plant characteristics of small sections (soil moisture, nutrient level of soil, soil structure, product requirements, yield, etc.). As a result, Precision agriculture technologies are agricultural production and management methods whose targets are more economic and more environmentally sensitive production [2].

Precision agriculture practices start with the acquisition of data through the use of various sensors and remote sensing technologies and continue with the determination of soil properties of the production area through soil tests. All information such as yield values, fertilizer and pesticide application norms, climatic data, topographic data, weed density, disease status of the previous production seasons are associated with their actual location in the production area. Then, the applications to be done are decided using appropriate hardware and software. And, it ends with the application of variable-level practices in the field according to the application form decided. In addition, variable rate application systems and real-time product monitoring systems have been developed as a result of the sensors and software developed by the manufacturers of precision agricultural equipment and technologies:

a) Increased production efficiency,

b) Improved product quality,

c) The use of more effective chemicals and other inputs,

d) Energy saving,

e) The soil and ground water protection.

In addition to the production of field crops, precision agriculture technologies have been successfully applied in vineyards and orchards, pasture and meadow management and in animal production. Applications vary from tea industry in Tanzania and Sri Lanka to sugar cane production in Brazil, rice in China, India and Japan, grain and sugar beet production in Argentina, Australia, Europe and the United States [3]. Although it is expressed using different terms such as precision agriculture, precision farming, smart farming, variable rate application, site specific farming, site specific management, computer aided farming and prescription farming, the term smart farming has become more widely used recently.

Figure 1: A typical crop growing cycle in precision agriculture [5] modified [4].

The precision agriculture, or the knowledge-based management of agricultural production systems, has emerged in the mid-1980s as a method for implementing the right process at the right time in the right place. The increased awareness of the variability in soil and product conditions has been combined with emerging technologies such as global navigation satellite systems, geographic information systems, and microcomputers. In the beginning, precision agriculture has been used to adapt the fertilizer distribution to the variable soil conditions in the agricultural area. Since then, additional applications have been developed, including the automatic steering applications of agricultural vehicles, autonomous machinery and processes, product monitoring, farm research and software for the general management of agricultural production systems. A typical crop growing cycle in precision agriculture is shown in Figure 1 [4].

Precision Livestock Farming

The first desired condition in animal production is breeding races with higher meat and milk yield. Second one is to make sure that the highest level of individual potential of animals is achieved through an adequate and balanced nutrition. The third is to take preventive health measures against diseases that cause the major losses in animal production and to minimize the use of drugs with the early detection of diseases and the necessary intervention [5]. Precision livestock production practices have contributed significantly to the solution of the problems experienced in animal breeding and in increasing the desired yield and quality in meeting the increasing animal food needs. Effective decisions are made by using precision livestock production practices in animal production and by monitoring individual animal conditions (amount of mobility, water consumption, milk conductivity value, amount of milk, etc.); necessary health measures are taken as soon as possible with the early identification of negative changes in animal health; and, sustainable and productive management is provided by ensuring that the individual potential of the animals is utilized at the highest level by making the herd management applications accurate and timely [6].

Precision livestock production allows collecting data at individual cow level as well as precision (individual) nutrition, regular milk recording (yield and components), pedometer, pressure plates, milk conductivity indicators, automatic oestrus detection, body weight, temperature, lying behavior, ruminal pH, heart rate, feeding behavior, blood analysis, respiratory rate, rumination time and movement skill scoring using image analysis. In this way, it minimizes drug (antibiotics) use and provides and proactive animal health strategy through preventive health by focusing on health and performance [7]. Benefits from precision animal production technologies include increased efficiency, reduced cost, improved product quality, minimized negative impacts on the environment and improved animal health and welfare. These technologies are likely to have a major impact on health, reproduction and quality control [8]. Figure 2 shows the areas observed in dairy cattle in precision livestock production.

Figure 2: The areas to monitor in dairy cattle in precision livestock production [9].

Figure 3: The tasks of the automated control systems for dairy farming [10].

Automatic control systems developed for dairy cattle farms provide solutions to the following tasks (Figure 3) [10]:

a) Getting the current information about animals;

b) Fast access to the animal history;

c) Increasing the milk yield because of the preclinical disease diagnosis;

d) Structure analysis of the herd and the animal physiological condition;

e) Reducing veterinary medicine costs;

f) Detection of the breaches in the herd reproduction technology;

g) Reducing the number of unpregnant animals and increasing the calf’s productivity;

h) Increasing the feeding effectiveness;

i) Reducing work costs and the improvement of work culture.

Autonomous Tractor

The concept of autonomous refers to the functions performed by the tractor without any human intervention. The concept of autonomous tractor and automatic steering should not be confused with each other. A tractor with automatic steering requires an operator for safety, avoiding unknown obstacles and performing unspecified tasks. An autonomous tractor can operate without the operator in overcoming the numerous uncertainties in the agricultural environment. In autonomous tractors, the necessary hardware and software are developed for obstacle avoidance, localization and mapping in addition to determining algorithms, models and methods for movement control. In order to implement route planning and navigation for this purpose, it is necessary to accurately estimate the position of the vehicle and to detect the environment sensitively during the movement of the vehicle.

Figure 4: Safety sensor for an autonomous tractor [11].

Various equipment and systems are used to determine the position of autonomous tractors, to set the desired route correctly, and to map the obstacles and objects around correctly. The information collected from the sensors should allow the autonomous tractor to move safely. Since autonomous tractors operate in outdoor and in diverse environments, errors may occur due to inability to receive information from some of the sensors or due to the errors in the information received. For this reason, it is preferable to process the data from different sensors together and unique advantages of the different types of sensors are used together to obtain a more comprehensive perception (Figure 4). In this way, the information from the sensors provide more detailed information about the location, environment and surrounding objects during the movement of the tractor. And, in order to turn this information into useful information, advanced decision mechanisms, utilizing applications such as image, audio and video processing algorithms, neural networks, machine learning, statistical data analysis, are used and the autonomous tractor is operated successfully in this way. The equipment used in autonomous tractors are listed below:

a) Radar Sensors

b) Laser Scanners,

c) Lidar,

d) GPS / Inertial Navigation System,

e) Ultrasonic Sensor,

f) Cameras.

When developing an autonomous tractor, combining a large number of tasks to increase operational success will relatively facilitate the task. These tasks include [12]:

a) Coordination: The coordination of multiple vehicles can be done centrally. Each vehicle operates independently and does not know necessary information about other vehicles, but it has its own tasks to fulfill.

b) Solidarity: Solidarity refers to the awareness of multiple vehicles, working in the same field, from each other and tasks of the others. For example, if three vehicles carry out the same task, such as clearing the same area from the weeds mechanically, then each vehicle needs to know the rows in which other vehicles were running before selecting a new row to begin. It would not make sense to have two vehicles come to head-to-head at the same time. Real-time communication is needed between the paired vehicles.

c) Cooperation: It refers to multiple vehicles sharing the same task at the same time. Using multiple vehicles to pull a large trailer that a vehicle cannot pull alone is an example of cooperation.

Agricultural Robots

Agricultural robots are classified as indoor and outdoor robots, in general. Outdoor robots include GPS assisted steering systems, meadow robots, pruning robots, spraying robots, seeding/planting robots and silage robot. Indoor robots include harvesting robots, milking robots and barn robots [13]. Autonomous agricultural robots are now an alternative to tractors in the fields. Breeding operations can be carried out by the fleets of autonomous agricultural robots in the future, such as seed sowing, spraying, fertilization and harvesting robots. Agricultural robots must have some basic capabilities and the ability to support multiple applications. A navigation system is required for safe and autonomous navigation as a basic capability [14]. When different applications of autonomous vehicles in agriculture have been compared with conventional systems, it has been found that the first three main groups of potential practical applications include plant cultivation, plant care and selective harvesting [15].

In the last two decades, special sensors (machine vision, GPS, RTK, laser-based devices and inertial devices), actuators (hydraulic cylinders, linear and rotary electric motors) and electronic equipment (embedded computers, industrial PC and PLC) have integrated into numerous autonomous vehicles, especially the agricultural robots. These semi-autonomous/autonomous systems provide correct positioning and guidance in precision agricultural tasks, when equipped with appropriate equipment (agricultural tools or equipment) [16]. Field map can be generated by estimating the location of the plants in the surrounding environment through image processing and recorded data detected by sensors. The position estimation of the robot can be done by a navigation system or relative calculation of the movements of the robot. The distance of the plants to the robot can also be detected by sensors or image processing, and the calculated positions can be marked on a map [17].

The Use of Unmanned Aerial Vehicles in Agriculture

Aerial vehicles that can operate through remote control or autonomously with its own power system, and that can load and unload payloads depending on the place of use are called Unmanned Aerial Vehicles (UAV). There are two types of aerial vehicles, including UAVs that can fly autonomously on a certain flight plan and remote controlled drones. Although these vehicle names are commonly used interchangeably, the term UAV is a general term for all unmanned aerial vehicles, whether autonomous or remotecontrolled.

A typical UAV system consists of the aircraft, one or more ground control stations and/or mission planning and control stations, payload and data connection. In addition, many systems include launch and recovery subsystems, aerial vehicle carriers and other ground services and maintenance equipment. A very simple general-UAV system is shown in Figure 5. Being more complex and having more parts than drone systems increase [18] the cost of system installation of UAVs. In drone systems, however, drones can be used immediately after purchasing drones together with the apparatus without the need for any other costs. Due to the lower cost of purchasing than the UAVs, their ease of use and their capabilities, drones are preferred in agricultural applications.

Figure 5: Generic UAV system [18].

Drone systems provide fast and safe solutions and analysis for numerous situations, particularly for military applications, including natural disasters, monitoring of various sports activities, traffic control, wildlife monitoring, and agricultural applications. Therefore, drone systems are produced in different formats according to their area of use. One of the most preferred applications of drone systems is the four-rotor drone system known as the quadrotor shown in Figure 6. Quadrotor, as the name suggests, is a general term of the drone systems with four independent rotors. The most important advantage of the quadrotor is its high maneuverability. This superiority gives the quadrotor the capability of vertical takeoff and landing in dangerous and confined spaces. Due to the highpower consumption of four rotors of a quadrotor, it cannot perform long-term flight duty. The capacity of the device can be increased by increasing the number of rotors. Six-rotor hexacopters and eightrotor octocopters are the examples of different forms of quadrotor obtained by increasing the number of rotors [19].

Figure 6: Four-rotor drone system [20].

Increasing productivity and improving product quality in agricultural production depends on good monitoring of the plants’ development process and taking the necessary actions at the most appropriate time. Drone systems, which have a simple technical structure and are easy to use, offer farmers an opportunity to make plans in agricultural activities using their embedded sensors and cameras, providing high quality and 3D images. Varies studies are carried out with drone systems, such as product development monitoring, plant species separation, crop harvest determination, automatic harvest, drought, detecting diseases, agricultural pests, etc., damage detection, fruit and vegetable and soil moisture classification, field management, organization of agricultural activities, and agricultural insurance [21].

Drone systems have 5 effective use areas in agriculture. These are [22]:

a) Product status monitoring: Farmers can inspect their growing products faster and more effectively with drones with NDVI or NIR sensors.

b) Irrigation systems monitoring: Large enterprises are able to monitor irrigation systems for the supply of water needed for certain products such as corn, which are spread over large areas, after having reached specified sizes.

c) Weed identification: Weed maps are generated by postprocessing the flight images and NDVI sensor data. In this way, farmers can easily distinguish between high density weeds growing together with healthy plants.

d) Variable rate applications: Variable-rate maps are rapidly and practically generated with the use of NDVI sensors in drone systems, instead of using variable-rate application maps prepared by ground-based or satellite images. In this way, it is possible to increase the efficiency by decreasing fertilizer costs.

e) Herd management and monitoring: The amounts and activity levels of free-bred ovine or bovine animals can be monitored from above through a drone.

Conclusion

Agriculture is a vital industry due to its contribution to the sustainability of lives of people, to national income and employment and its provision of raw materials to other industries. Therefore, the agricultural sector has a direct impact on all segments of the society with its economic, social and environmental dimensions. Economically, subjects such as increasing agricultural production and farmer revenues, minimum use of production inputs, improving marketing conditions, etc. are addressed. Socially, there are topics such as food quality and safety, agricultural employment, socio-economic sustainability of rural areas, animal welfare, etc. And, environmental issues include biodiversity, protection of wildlife, meadow-pasture, forests, underground and surface waters, and soil resources. Utilizing the opportunities offered by advanced technologies is becoming increasingly mandatory in order to achieve high success in studies conducted on all these comprehensive issues, due to the importance of the subjects and difficulties involved.

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Lupine Publishers | “Role of Agriculture in Ayurvedic Drug Research”

   Lupine Publishers |Agriculture Open Access Journal

Mini Review

Ayurveda which is known as science of life, is beauty of Indian culture. From ancient era Indian people live this science. Ayurveda given first preference to prevention than cure. Bhaishajya (doctor), Rugna (patient), Aushadha (medicine/drug), Parichrka (medical assistance) is four chikitsapaad of chikitsa [1]. Aushadha (medicine/ drug) is one of the important chikitsapaad among them. Nowadays golden days of Ayurved are arrives. CCRAS and AYUSH all are engaged in Ayurved research. There are four types of research, drug research is one of important research among them. Drug research in Ayurved done with modern as well as ancient parameter. For drug research source of plants is necessary. Original raw material of herbal drug can supply from special Ayurvedic agriculture. Drug research include proper identification, lit. study, filed work, cultivation, collection, testing efficacy of various part and body of plants, adverse effect, adulteration, morphology, photochemistry, study of various formulation and testing action of drug. [2].

In farming India has second rank in whole world. Due to various climate, soil, geographic structure variety of vegetable, fruits, plants production occurs in india.in exporting also our country is at top rank [3] India has shown a steady average nationwide annual increase in kilograms produced per hector for some items of agriculture. For treatment Ayurvedic physician need different types of fresh and qualitative drugs. Unfortunately most of drugs are unavailable or available with adulteration. Due to popularity of Ayurveda demand of herbal drugs is increased, but supply are less as per need, so marketing peoples do adulteration in herbal drugs. If raw so material of medicine is not pure, than how can we expect proper results? So, there is need of development of trained agriculture filed as per medical Science i.e Ayurveda.

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Lupine Publishers | Economic Analysis of Poverty Status of Small-Scale Farmers in Bayelsa State, Nigeria

  Lupine Publishers |Agriculture Open Access Journal

Abstract

The study analyzed the household poverty status of small scale farmers in Bayelsa State, Nigeria using a multi-stage random sampling technique to sample six hundred farmers. Data were collected using structured questionnaire and were analyzed using descriptive statistics, FGT [1] index and the logistic regression model. The result revealed that the majority of the farmers 80% were females, while 79% of the respondent was married with 46% of them having no formal education. Twenty-seven (27) percent of the crop farmers are poor while thirtyeight (38) of the livestock farmers were poor. Also, the poverty depth and severity of crop farmers were 0.072 and 0.038 respectively whereas they were 0.098 and 0.052 respectively for the livestock farmers. The logistic regression model revealed that age, educational level, household size, farming experience, farm/herd size, household income, household expenditure and membership of cooperative contributed significantly in determining the poverty status of the farmers. This study therefore recommends measures needed to be put in place to encourage and improve the welfare of the farming household towards productive and sustainable agricultural development for poverty reduction.

Keywords: Economic; Poverty; Status; Small Scale; Farmers

Introduction

Nigeria is a vast country endowed with substantial natural resources which include; 68 million hectares of arable land, fresh water resources covering about 12 million hectares, 960 million hectares of coastline and ecological diversity that favor the production of a wide variety of crops, livestock, forestry and fisheries product [2]. These coupled with its 37 million hectares of natural forest and rangeland and total land mass of 923,768km2 [3] makes agriculture one of the prominent sub-sector. In spite of these resources’ endowment, the productivity of agriculture continues to dwindle. One of the major problems confronting Nigeria today is how to improve the quality of life in the rural areas and reduce the level of poverty [4]. Poverty in Nigeria is not only a state of existence but also a process with many dimensions and complexities [5]. The report of the 2006 Nigerian Core Welfare Indicator (CWI) on the poverty profile in the country stated that the dependency ratio, which was defined as the total number of household members aged 0 – 14 years and 65 years and above to the number of household members aged 15 – 64 years was 0.8 Central Bank of Nigeria [6]. This indicated that almost a one-to-one dependency ratio and reflected the high population growth rate in the country. There is also large income inequality with the top 10% of the income bracket accounting for close to 60% of the total consumption of goods and services [7].

The World Bank [8] describes poverty to comprise of many dimensions. It includes low incomes and the inability to acquire the basic goods and services necessary for survival with dignity. It encompasses low levels of health and education, poor access to clean water and sanitation, inadequate physical security, lack of voice and insufficient capacity and opportunity to better one’s life. It may also result in not having enough capacity to feed and clothe the family and/or earn a living. About 90% of the country’s food is produced by small scale farmers cultivating tiny plots of land who depend on rain fed agriculture [9]. According to Omonona [10], poverty is pervasive although the country is rich in human and material resources that should translate into better living standard. Despite its plentiful resources and oil wealth, poverty is widespread in Nigeria [11]. The situation is said to have worsened since the late 1960s, to the extent that the country is now considered one of the 20 poorest countries in the world. Over 70% of the population is classified as poor, with 35% living in absolute poverty. Poverty is especially severe in the rural areas, where social services and infrastructure are limited or non-existent. Majority of those who live in rural areas are poor and depend on the agriculture for food and income.

The concern on the threat posed by poverty has led the Nigerian government over the years to devote considerable attention to alleviating its scourge through various policy projects and programmes which seems not to have stem the ugly situation till date. In view of these, the question about the poverty status of rural dwellers especially the small scale farmers remained unanswered. It is on this premise that this study was carried out to answer these questions.

a) What are the socio-economic characteristics of these small scale farmers?

b) Are the small-scale farmers really poor?

c) What are the factors influencing poverty status of the small scale farmers? and

d) What options are available to small scale farmers in reducing their poverty levels?

Thus, the main objective of this study is to evaluate the poverty status of small scale farmers in Bayelsa State, Nigeria. The specific objectives are to:

a) Describe the socio-economic characteristics of small scale farmers.

b) Compare the poverty status of crop and livestock farmers in the study area;

c) Determine the factors influencing poverty status of the farmers; and

d) Make policy suggestion towards poverty alleviation.

Methodology

Study Area

The study was conducted in Bayelsa State, Nigeria. It is located between latitude 5° 00¹ to 10° 30¹ N and longitude 4° 55¹ to 6° 00¹ E and covers an estimated land area of 1,810km² with a population of about 856,729 thousand [12]. It shares local boundary with Delta State, Anambra State and Rivers State with the Bight of Benin at the Southern Flank. There are eight (8) Local Government Areas (LGAs) in the state with Ijaw as their major language. Mean annual rainfall of the area is 2,200mm for upland or dry regions where water bodies are few and 3,500mm for wetland or lowland region which comprises of land areas being surrounded by water bodies. Temperature range is between 23 – 31°C and vegetations found in the area include the saline water swamp, mangrove swamp and the rain forest. Major seasons are the dry (November – February) and wet seasons (October – March). Also, the seasonal condition of the area presents a healthy environment for farming which is the main source of income and livelihood of the state’s population and agriculture accounts for about 72% of the labor force.

Sampling Procedure and Sample Size

The sampling involved a multistage random sampling technique. Firstly, six (6) local government areas were randomly selected using the proportionate sampling method at 75% precision level from the purposively selected three (3) agricultural zones according to Agricultural Development Programme (ADP) structure. In the second stage, ten (10) villages were also randomly selected from the six (6) LGAs each making a total of sixty (60) villages. The third stage involved a simple random selection of five (5) crop and five (5) livestock farmers each from the villages using the list provided by ADP from each of the villages. A total of six hundred (600) respondent farmers were used.

Data Collection

Both primary and secondary data were used for the study. The collection of primary data was achieved using a set of structure questionnaire that was administered by the researcher and trained enumerators complemented with oral interview, information that was collected covered the areas of socio–economic characteristics, farming operations, and income and expenditure patterns. Secondary data were sourced from relevant material both published and unpublished.

Analytical Technique

Three analytical tools namely; descriptive statistics; Foster, Greer and Thorbecke (FGT) model of poverty decomposition [1]; Logistic regression were used for the study. Descriptive statistics such as frequency, percentages, mean were used to profile the socio-economic characteristics of the farming households and also to present the results of the findings. The FGT measure was used to assess the incidence, depth and severity of poverty of the farming households. The approach makes use of the aggregate values of the poverty indices – poverty headcount, poverty gap, and squared poverty gap. The use of the FGT measures required the definition of poverty line and this was calculated on the basis of aggregated data on household income. The FGT measure as used by Baiyegunhi and Fraser [13] is expressed as:

Where:

z = Poverty line

m = Number of households below poverty line

n = number of households in the reference population

yi = Per adult equivalent income of ith household

α = Poverty aversion parameter

z-yi = Poverty gap of the ith household

z – yi = Poverty gap ratio

The headcount index was obtained by setting a = 0, the yield poverty gap index when a = 1, and squared poverty gap index when a = 2. The poverty line is a predetermined and well defined standard of income and value of consumption. In this study, the poverty line was based on the income of the households. A relative poverty line was used in which a household was defined as poor relative to others since they are all farmers. Two third of the mean per capita income (MPCI) was used as a moderate poverty line while one third was taken as the line for extreme poverty. Thus, the farming households were grouped into three categories based on their levels of poverty: the extremely poor (those whose income was less than one-third of MPCI), the moderately poor (those whose income lies between onethird and two-third of the poverty line) and the non-poor (those whose income was above two-third of the poverty line).

Adult equivalents were generated following Nathan and Lawrence [14], thus:

AE =1+ 0.7(N₁ −1) + 0.5N₂

Where

AE = Adult equipment

N₁ = Number of adults aged 15 years and above

N₂ = Number of children aged less than 15 years.

Logit Regression Model

A binary logistic regression model was used to analyze the determinants of poverty. Thus, poverty is the dependent variable and is determined by independent variables such as socioeconomic characteristics of households and access to services. The dependent variable is binary (1 if the household is poor and 0 if the household is non-poor). The logit model is based on the cumulative logistic distribution function expressed as:

Where:

L_i = log of the odd ratio, which is not only linear in Xi but also linear in the parameters,

P_i = is the probability of being poor and ranges from 0 to 1.

Z_i = the function of the explanatory variables (x) which is expressed explicitly as:

Where:

Bo = Intercept, Bi – B₉ = coefficient of the independent variables, xi = is the vector of relevant independent variables and U = is the stochastic error term, z = the dependent variable defined as the mean annual per capita expenditure. It was measured in binary terms such that 0 = poor, that is if the mean per capita household expenditure is below the poverty line and l = not poor, that is if the mean per capita household expenditure is above the poverty line and:

X₁ = Age (number)

X₂ = Farm size (number of herds/hectares)

X₃ = Marital Status (1 = Married, 0 = otherwise)

X₄ = Household size (number)

X₅ = Education Level (number of years)

X₆ = Major Occupation (1 = farming, 0 = otherwise)

X₇ = Farming Experience (years)

X₈ = Household income (Naira)

X₉ = Household Expenditure (Naira)

X₁₀ = Extension contact (1 = yes, 0 = otherwise)

X₁₁ = Cooperative Membership (1 = yes, 0 = otherwise)

Results and Discussion

Socio-Economic Characteristics of Respondents

Table 1 shows the socio-economic characteristics of the respondents. The majority, 80.3% of the respondents were female while 19.7% were male. This suggest that majority of small scale farmers in the study area are female. About 61% of the respondent were age <30 to 50years with the mean age of 41 years. These results suggest that majority of the farmers were in their active productive age. Moreover, 79.17% of the respondent farmers were married, only 14.83% were single and 6.00% of the farmers were either divorced or widowed. About 46.50% of the farmer does not have formal education, while 32.67% had primary education. Only 16.17% and 4.67% of the respondents had secondary and tertiary education respectively. These result confirm the low level of education in the study area as the state was rated as an educationally disadvantage state in Nigeria. Majority of the respondent farmers 65.67% had a household size ranging between 6 – 10 persons, while 11.50% had less than 5 persons and 22.83% of the respondent had above 10 persons. The result suggests a large household size among the respondent farmers (Table 1).

Table 1: Socio-Economic Profile of Respondents.

On the basis of farming experience, about 27.50% of the respondents had less than 10 years farming experience, while 39.50% had farming experience ranging between 11 – 20 years. Only 31.17% had farming experience ranging between 21 – 30 years and 1.83% had farming experience of more than 30 years. Based on household income, about 71.17% of the respondents had annual income ranging between N100,000 – N500,000, while 25.33% had annual income ranging between N501,000 - N1,000,000, only 3.50% of the respondents had annual income above N1,000,000. Majority of the farmers 63.00% had household expenditure ranging between N501,000 – 1,000,000, while 22.83% had expenditure ranging between N100,000 – N500,000 and 14.17% of the farmers had household expenditure above N100,000. These result suggest that majority of the respondent farmers spend more than they earn thereby pushing them more into poverty. Majority of the farmers 85.83% have no access to extension services while only 14.17% of the farmers have access to extension services. Moreso, 65.67% of the respondent farmers are members of cooperative societies while 34.33% do not belong to cooperative society (Table 2).

Table 2: Poverty Incidence, Depth and Severity of Respondents.

Analysis of Poverty Status of the Farmers

Table 2 shows the summary of the poverty incidence (P0), depth (P1) and severity (P2) among the respondents. The MPCI of the crop farmers was 21,017.20. This gives a moderate poverty line (2/3 MPCI) of 14,011.47 and a core poverty line (1/2 MPCI) of 7005.73. The MPCI of the livestock farmers was 17,213.10. This gives a moderate poverty line (2/3 MPCI) of 11,475.40 and a core poverty line (1/2 MPCI) of 5737.70. Hence, crop farmers whose monthly per capita income falls between 14,011.47 and 7005.73 were regarded as moderately poor while those who fall below 7005.73 were regarded as core poor and those above 14,011.47 were regarded as non-poor. For the livestock farmers, households whose monthly per capita income fall between 11,475.40 and 5737.70 were regarded as moderately poor while those below 5737.70 were regarded as core poor and those who are above 11,475.40 were regarded as non-poor. The poverty incidence (Table 2) shows that among the crop farmers, 27% of the populations were poor while among the livestock farmers, 38% of the populations were poor. The poverty depth of the crop farmers and livestock farmers was 0.072 and 0.098 respectively. This implies that they would need to be increased by 7.2% and 9.8% respectively for them to come out of poverty and become non-poor. The poverty severity measures the distance of each poor person to another. Among the crop farmers, the distance was 0.038 while in the livestock farmers the distance was 0.052. Overall, a comparison of the poverty status of the crop and livestock farmers indicated that the poverty status is relatively close even though it is higher among livestock farmers. The result may not be unconnected to excessive expenditure incurred by head of household as a result of increase household size and low-income occasion by subsistence nature of farming.

Factors Influencing Poverty Status of the Respondents

Table 3 shows the factors influencing poverty status of the farmers. The regression classification table revealed that the binary logistic model predicted 97% of the regression correctly. The model fits the data at (P<0.001) as indicated by the chi-square goodness of fit statistic (73.28). The goodness of fit of the model proved that the variables tested in this study were valid to explain the determinants of poverty in the study area. Besides, the Nagelkerte R2 value (0.867) shows that about 87% of the outcome (Likelihood of being poor) can be explained by the selected independent variables captured in the model (Table 3).

Table 2: Logistic Regression Result on Factors Influencing Poverty Status of the Respondents.

Percentage Prediction = 97.57%

Goodness of fit chi-square (df=11) = 73.28 (P<0.001) Nagelkerte R2 = 0.867

***, ** and * = figures significant at 1%, 5% and 10% levels respectively.

Source: Computation from field survey data, 2017.

The results of the regression model indicated that eight (8) of the eleven (11) explanatory variables influenced the poverty status of the farmers. The variables were age, educational level, and household size, farming experience, farm/herd size, household income, household expenditure and membership of cooperative. The coefficient of age of the farmer was significant and negatively related to the probability of a household becoming poor. This implies that the age of the farmers is a causative factor of poverty. As age of the farmers increase, the likelihood of being non poor is reduced. This conforms to a priori expectations and work by Ayalneh [15], Obiesesan [16], who opined that older households had greater likelihood of being non-poor. This may be attributed to increased experience and exposure to farming operations and management practices as their age increases.

A positive and significant relationship was found between educational qualification and the likelihood of being non-poor, hence, the higher the educational level, the lower the tendency of been poor. The result is in conformity to a priori expectations and work by Ogwunike [16] who found that a positive significant relationship existed between educational level and the probability of being non-poor. The coefficient of household size was negative and was significant at 1% level. This implies that, the higher the household size, the more likely to become poor. Ceteris paribus. This could be as a result of the fact that the members of such households would have to depend on the limited resources that is available to the household thereby reducing the per capita income of the household. This is in agreement to a priori expectations and work by Khan [5] and Ogwumike [16].

A positive and significant relationship was found between farming experience and the likelihood of being non poor at 5% level. This implies that the higher the years of farming, the higher the probability of being non-poor. This is in conformity to a priori expectations and work by Omonona [10] who stated that exposures and experiences gathered over the years help rural poor people to fight poverty. The author further opined that experience in farming help to reduce losses thereby encouraging proper handling and management of relatively scarce resources. There was a positive and significant relationship between farm/herd size and the likelihood of being non-poor. This implies that as the farm/herd size of the farmer increases, the probability of the household being nonpoor is increased. This finding conforms to a priori expectations and work by Eneyew [17] and Alemu [18] who found that a unit increase in land holding increased the probability of being nonpoor. The coefficient of household income was significant at 1% level and positively related. This implies that as the household income increase, the probability of being non-poor increases. This is in agreement to a priori expectations and work by Alemu [18] who found a positive relationship between household income and the likelihood of being non-poor.

In conformity to a priori expectations, the coefficient of household expenditure was negative and significant at 5% level. This indicated that, the higher the household expenditure, the lower the likelihood of being non-poor. Ogwumike [19] stated that, excessive expenditure by household head is a pointer to poverty. The coefficient of membership of cooperative was positive and significant at 5% level. This implies that, if a household head is a member of cooperative, the likelihood of being non-poor increases. This will not be unconnected with the fact that members of cooperative in the rural settings help their cooperative members in time of needs and also provide incentive and loan facilities to those in need.

Conclusion

The research has shown that, the incidence, depth and severity of poverty were high among the farming households even though some of the farmers fall above the poverty line. The study has also shown that the rate of poverty is relatively higher among livestock farmers compared to crop farmers. Meanwhile, the study has revealed that several factors influences the poverty status of the farming households such as age, educational level, household size, farming experience, farm/herd size, household income, household expenditure and membership of cooperative [20].

Given these findings, therefore, it is recommended that:

a) Government and other relevant non-governmental organizations should provide incentives and infrastructures that will enhance productive and sustainable agricultural development in the rural areas.

b) The farming households need to diversify their productive activities through mixed farming and value addition to improve their non-farm income thereby reducing poverty.

c) Policy makers and the operators of rural economy should carefully understand those variables that influence the poverty status of the farming households and address them critically and vigorously.

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Lupine Publishers | Dimethyl ether as Zero Emission Fuel-Synergies with Biogas and Biomass Plants

    Lupine Publishers |Agriculture Open Access Journal

Introduction

In Iceland a methanol plant named in honour of the noble prize laureate [1] operating since 2011. As substrate they use carbon dioxide and hydrogen producing methanol. Methanol is the simplest alcohol and well know since the developments of Paul Sabatier and the catalysis processes [2], in liquid phase at environment pressure and temperature, and is a synthetic alcohol. In the Georg Olah Plant [3] carbon dioxide and hydrogen are mixed 1mol:3mol together to form a syngas, being compressed and transformed under the help of catalysts to methanol (methanol synthesis).

.

In most processes the methanol synthesis is running at a pressure range 30 bar up to 100bar and a temperature range 200 °C up to 400 °C. The conversion rate is given in the range of 25% up to 35% and therefor recycling of the unconverted gas in the methanol reactor back, to increase the conversion rate of synthetic gas and production rate. Leaving the methanol reactor, the product gas will be cooled down andthe condensate mixture of water and methanol is distilled and separated into water and product methanol. The methanol synthesis with carbon dioxide hydrogen is needed, generated by wet electrolysis.

Figure 1: Methanol and Dimethyl ether from carbon dioxide and water.

From water and the electric power needed for the electrolysis is generated by geothermal heat conversion to electricity (Figure 1). This is a special property of Iceland. Now the question arises, where does the carbon dioxide come from? In the most common case. Carbon dioxide is separated from exhaust gas from fossil fuelled power plants and industrial processes. Using fossil carbon dioxide in plant process the George Olah plant [3] is now accelerating the consumption of fossil fuels if we use methanol as a fuel. Therefor methanol should be used in chemical industry fixing carbon dioxide [1]. But if we use methanol as fuel in transportation, the combustion of methanol leads to carbon dioxide and water being transferred to normally carbon dioxide transferred to the environment is a dilution of carbon dioxide in the air. We watch that methanol burned in a classical Otto motor cycle additional produces compared to fossil diesel fuel in a diesel engine higher pollution, dust, soot and a higher amount of carbon dioxide in the exhaust gas. Therefor methanol is converted to dimethyl ether by extraction of water under acid conditions [4,5].

Dimethyl ether is often mentioned as the ideal Diesel fuel [8], tested over long years from VOLVO [6] and by MACK TRUCK [7] in heavy trucks on the road. Dimethyl ether is the simplest ether a synthetic fuel, certificated by the ISO 16 681:2013 by the IDA, produced from methanol or by direct synthesis (Figure 2).

Figure 2: Mack truck testing Dimethyl ether.

In most cases there is no application of methanol in transport, civil, agriculture and forestry, because they are running on fossil diesel. Heavy strong robust power machines are needed and the diesel engine is the ideal power machine. Methanol cannot substitute fossil diesel directly. But dimethyl ether has this needed property. As shown from MACK TRUCK (New York) [7] testing Dimethyl ether in heavy trucks [7]. Since VOLVO (Sweden) [6] started in using Dimethyl ether in heavy trucks in 2008, running over five years the trucks on the road (Figure 3), and moved then to the USA at MACK TRUCK [7], it is well known that dimethyl ether is a story of success and dimethyl ether is the ideal Diesel fuel [6,7].

Figure 3: VOLVO heavy truck running on Dimethyl ether.

Ethanol, Biodiesel

Using corn from agriculture bioethanol is produced with fermentation. Corn is a food product not agricultural waste. Bioethanol has the same combustion and emission problem as methanol: it can only be used in a gasoline engine and leads to higher pollution, lower efficiency, soot dust, and high carbon dioxide than dimethyl ether. In Europe biodiesel is mixed with fossil diesel. Biodiesel is produced from oil and fatties over catalytic esterification, but again biodiesel has the same combustion problem as methanol: although biodiesel can be used in diesel engines, biodiesel leads to higher pollution, lower efficiency, soot dust, and high carbon dioxide than dimethyl ether [6].

Biogas

The anaerobic fermentation process enables to produce biogas, consisting of methane and carbon dioxide (CH4, CO2). Biogas can be produced from wet biogenic waste. The anaerobic process can be realized in wet phases or in dry phases but always lead to biogas and digestate, which can be recycled again. In most application biogas is used to generate electricity and heat. The electric efficiency of biogas engines is 30% up to 36%, and we have an exhaust gas, therefore no zero emission.

Forestry biomass

In Forestry wood is used for pulp and paper and for wood in civil and industry. Generating heat from wood chips with a warm water boiler is well known. In the most application biomass is used to generate heat. The thermal efficiency is low 75% up to 85%, and we have an exhaust gas and again no zero emission.

Reforming and gasification for dimethyl ether

Dimethyl ether can be produced from biogas and biomass. Biomass as waste biogenic mass can be used for gasification to generate synthetic gas and char coal. The char coal is carbon, the synthetic gas consists of CO:23%, H2:20%, CH:1%, O2 <0.1%, CxHy: 3%, Rest CO2. The heat caloric value is about 1.5kWh/m³. Charcoal can be reused again and converted to syngas over the known water gas reaction

Biogas can be used to generate synthetic gas with dry reforming:

The synthetic gas consists of CO:40%, H2:40%, CH:3%, O2< 0.1%, CxHy: 1%, Rest CO2. The heat caloric value is about 2.5kWh/ m³. In both cases syngas can be transformed to dimethyl ether over direct synthesis: 3CO + 3H2CH3OCH3 + CO2 + Q (- 254kJ/mol).

Hydrogen

Figure 4: Dimethyl ether and SOFC Cycle.

Cheap hydrogen is the basic requirement for the production of cheap and competitive dimethyl ether from methanol (Figure 4). Hydrogen from electrolysis costs electric power ~5.0 kWh/m³ H2. Hydrogen generated from waste heat, enables to split water into hydrogen and oxygen with metals at temperatures from 400 °C up to 800 °C:

SOFC or thermionic and magneto hydrodynamic Generator Using dimethyl ether in a SOFC (solid oxide fuel cell) cell dimethyl ether has to be converted to syngas by steam reforming

The exhaust gas from the SOFC cell consists of carbon dioxide and steam.

SOFC cells operate in a temperature range 800 °C up to 1000 °C, at nearly environment pressure and have an electric efficiency of 50% up to 60%. Another possibility is to generate heat with combustion of dimethyl ether in a metal oxide reactor.

The generated heat can be direct converted to electric energy with a thermionic generator. Thermionic generators have an electric efficiency from 25% up to 35%, combined with magneto hydrodynamic generators having an electric efficiency from 30% up to 40%, we gain in sum from 55% up to 75% for the direct conversion of heat to electric energy (Figure 5). In both applications we oxidize dimethyl ether to carbon dioxide and water under pressure up to 50 bars.

Do that the exhaust gas consisting of carbon dioxide and steam can be collected as condensate in different tanks. This enables carbon dioxide and water to be reused in such plants like the George Olah Plant [3] and Oberon plant [4] again. The step of collecting carbon dioxide and water is the closure of the methanol over the dimethyl ether processes. It is now a closed cycle collecting carbon dioxide in a tank wo be recycled to methanol and dimethyl ether process again. This closed cycle now reduces the emission of greenhouse gas like carbon dioxide and can be seen as a sustainable property of the carbon dioxide recycling. Carbon dioxide now is a substrate and a basic part in the fuel production and not a pollution in the exhaust gas anymore. Under this conditions carbon dioxide and the emission certificates connected to carbon dioxide can be used in a global trade [8].

Figure 5: Dimethyl ether and high temperature heat generation.

Closing the cycle

To reach zero emission we must convert Dimethyl ether into carbon dioxide and water. Carbon dioxide and water can be converted back (recycled) to dimethyl ether with electric energy and heat. Under this cycle we generate only this amount of carbon dioxide, connected with dimethyl ether. Using more dimethyl ether enables to reuse more carbon dioxide and the process is acting like a carbon dioxide sink. Focusing on this property of zero emission enables to save energy and substrate in agriculture and forestry, in civil and transportation (Figure 6). Using waste from agriculture and forestry, using biogenic waste from hotels, food industry and biogenic waste from municipal and civil waste, reduces the pressure on new and fresh biomass, reduces the pressure on fossil substrates. Under the property of zero emission the methanol cycle of the George Olah plant [3] will be renewable and also the dimethyl ether plants of Oberon [4,9].

Figure 6: Dimethyl ether closed cycle.

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