eChapters

Abiotic Environment

The physical or abiotic environment includes all those physical and non-living chemical aspects, which exert an influence on living organisms. The abiotic components are soil, water, air and energy from various sources. These provide both habitation and raw material for the synthesis of organic food.

Bhuva is a small village in north-west Gujarat – India.  My mother was born there. The loving people of Bhuva called me ‘Bhano’ (Sister’s Son / Nephew) and it was my favourite place to go as a child. I have my fondest memories of Bhuva, but could not visit for several decades, until when I was once at the Junagadh Campus of Gujarat Agricultural University, to inaugurate the International Conference on New Developments in Agriculture. Bhuva was not too far and I decided to go there. Soon as I reached, a familiar face, now aged with time, recognized me from a distance and with tears rolling from his old eyes (and mine) he hugged me, calling out “MaroBhano, MaroBhano” (My sister’s son, my sister’s son)! Now in his eighties, it was overwhelming to meet Nanji Ata, who I always remembered being wise, educated and cheerful. He was a relatively progressive farmer with about 6 to 7 acres of land, a few cows and a buffalo. Though water was scarce and saline, he managed to make a good living and was considered a well to do man.

7.1 Summary

Climate change is a complex global phenomenon that poses significant challenges for agriculture including uncertainty about future weather patterns, soil conditions, and other environmental factors. Farmers are facing the difficult task of interpreting and responding to these changes in order to maintain their livelihoods and ensure food security for growing populations. In this context, mental modelling has emerged as a useful approach for understanding how farmers interpret and respond to climate change. Mental models are cognitive representations of how individuals understand and make decisions about complex systems, and can be used to identify key drivers of behaviour and decision-making.

The present study has explored the social, ecological, and economic factors that influence farmers’ mental models on perception of climate change, level of uncertainty and trend of agricultural change in the selected study area. It argues that farmers’ mental models are shaped by a range of factors, including their own experiences and observations, social networks and interactions, access to information and resources, and broader economic and political contexts. The present study has also highlighted the role of uncertainty in shaping farmers’ mental models and decision-making. Uncertainty about the future impacts of climate change can create significant challenges for farmers, as they must make decisions based on incomplete information and uncertain predictions.

Flooding and soil erosion are closely connected because concentrated runoff detaches, transports, and deposits soil material throughout a watershed and river system. Raindrop impact initiates splash erosion, while surface flow produces sheet, rill, and eventually gully erosion. Sediment generated from fields and channels reduces reservoir capacity, raises riverbeds, obstructs drainage, and lowers channel conveyance, thereby increasing flood risk. This chapter explains erosion mechanisms, classifications, influencing factors, gully development, sediment yield, sediment-delivery ratio, suspended and bed loads, and sediment-transport models. It also examines soil-loss estimation and agronomic or structural conservation measures that can reduce erosion, control sediment movement, and improve watershed-level flood management

Damage to arable land tends to attract most attention but degradation of non-arable land can also have serious effects, whether economic, or social, or on the food supply. In dry seasons, loss of grazing capacity is a common and serious problem, forest land may suffer through over-exploitation, roads can cause much damage particularly on steep land. In this chapter we will look at the more important erosion problems on non-arable land.

13.1 Erosion on grazing land

On well-managed high-yielding pasture it is unlikely that there will be any erosion, for the ground will have a good uniform cover. But land which is not arable because it is too poor, too infertile, too stony, or too arid, and is only described as grazing land because it is unfit for more intensive use, is a very different matter. Again, erosion control can be directly and precisely equated to improved management - anything leading to a better water balance and a better ground cover will lead to reduced erosion.

11.1 The farmer’s point of view

In the last chapter possible conservation practices were reviewed in groups according to their purpose in terms of influencing runoff or soil loss. Now, looking at ways of limiting soil degradation by improved land husbandry, the primary consideration is to look at the alternatives from the farmer’s point of view. In fact we need to be very wary of manual-type recommendations. A better approach is to offer a menu of alternatives, and the role of the professional is to help the farmer choose what is appropriate for his or her circumstances. A recent review of technologies acceptable to resource-poor farmers points out that combining practices into a farming system must take account not only of the physical factors such as soil and climate, but equally the available resource inputs, especially cash and labour, and the farmer’s objectives (STOCKING 1993).

The objectives of a commercial farmer are usually to maximize yield and income, but the subsistence farmer is likely to be more interested in improving food security by reducing the risk of failure, or improving the return on inputs of seed, fertilizer and labour, or improving the quality of life by reducing drudgery, particularly relevant in the case of women farmers.

We need a new approach to the farmer when recommending changes to farming practice. Instead of recommending practices because they will reduce erosion, we should show how they can lead to increased production. For example, practices which lead to better soil structure, more organic matter, or more moisture-holding capacity should be put forward because they improve yield, not because they reduce the soil erodibility. Increasing infiltration from better ground cover or the use of contour farming or ridging can result in better crops as well as reducing soil loss. Improved crop management could include more use of fertilizer and manures or crop rotations. These practices will make more sense to the farmer if they are recommended as leading to better production, which he can instinctively appreciate as desirable, rather than because they will reduce soil erosion which is likely to be a less important objective.

5.1 INTRODUCTION

A GIS database without single error is reliable for decision making. There are various possible sources of errors in GIS. If an error is not removed during scanning, the final output will also have that error. Besides whatever the work has been carried on wrongly scanned map it will add to errors because the source map itself's erroneous. This is like garbage in garbage out proverb. If wrong data is taken for analysis it will only result in wrong output. Hence it is important to remove error at every stage of GIS work. It is important to locate the sources of errors so that due care may be taken while handling a project.

5.2 ORIGINAL SOURCES OF ERRORS

All the equipments which give attributed data and spatial data will give errors. Before going to field or before collecting data all the related instruments should be calibrated properly. Also the instrument should not cross the permissible error. Besides these while using data from diversified sources errors pertaining to those instruments are mixed to give a different result. When very good accuracy is desired data should be collected from high end equipments. Even if one adds the data from a comparbly less accurate instrument to the data of many accurate instrument is then the result obtained finally will not reliable enough. Global Positioning System (GPS) is available with mm accuracy to meters accuracy. Also surveying with suitable map projection and datum is important; otherwise the result will be wrong. Survey instruments posses some inaccuracy and when a person is using it he should have the accuracy over the instrumental like rain gauge, water level recorders which give attributed data which should be checked for instrumental inaccuracies and method of data collection before using it. Likewise satellite and aerial sensors miss some data and some error are inherent. Such errors should be removed before moving ahead. Errors are resulted due to map reduction, enlargement, data editing and computation method adopted. At the same mapping instruments also possess inaccuracies. When after certain interval a person is going for survey, the registered object may have been changed. Many new features might have come up. Hence the established control points are changed and if the wrongly placed control points are taken for survey then the result itself will be wrong. If proper resolution is not available in the instruments, then the instruments may not capture the minute details which are required.

Introduction

The GIS database is a model of real world. It goes without saying that there is an inherent discrepancy between GIS database and the real world it represents. This is so because all models are fraught with cognitive issues and remain as approximations. To realize the objectives of a GIS, geographical data collected, entered and processed are to be sufficiently error free and reliable.

Errors can be statistical errors like mistakes, wrong inputs or faulty field surveys or system errors like excessive tolerances or high RMS error. The spatial database has further problems in the sense that the real world phenomena are not always intrinsically discrete. For instance where is the bank of a shallow river? Where is the boundary between two soil types? These are but a few questions revealing the fuzzy nature of geographic data. Many soil scientists, geographers have noticed that carefully drawn boundaries or iso-lines on maps in general give misrepresentations of change that are often gradual, vague or fuzzy. Traditional cartography as a medium of spatial data handling had a solution to such problems. But now when digital data are handled, they need to be stored with certain precision. Moreover, the spatial variation of natural phenomena is not just a local noise function or inaccuracy that can be removed by collecting more data or by increasing the precision measurement but is often a fundamental aspect of nature that occurs at all scales. (Goodchild, 1980; Mark and Aronson, 1984). This means residual unmapped variation is a remarkable problem in geographical information processing. It is therefore a prerequisite to fully understand how errors arise or propagated in GIS and what might be the effects of errors on output.

Terminology

    ·    Nutrient: Nutrients are substances required by an organism for their normal     growth and reproduction. 
    ·    Plant Nutrient: The plant nutrient is a “food” which is composed of certain chemical elements often referred to as ‘plant nutrient’ or plant food elements considered very essential for growth and development of plants. 
    ·    Nutrition: The supply and absorption of chemical compounds needed for growth and metabolism of an organism.

Agricultural meteorology, a discipline that has traditionally focused on understanding the relationships between weather and climate, and agricultural productivity, is at a critical juncture. The increasing vulnerability of agricultural systems to climate change, coupled with the need for sustainable and resilient agricultural practices, necessitates a fundamental transformation of agricultural meteorology. To support the development and implementation of climate resilient agriculture (CRA) practices, agricultural meteorology must be reoriented as a super speciality discipline. This requires a comprehensive overhaul of the discipline, including its curriculum, research focus, and practice. The reorientation of agricultural meteorology as a super speciality discipline is essential to provide the necessary expertise and knowledge to support CRA practices. This includes developing and applying cutting-edge technologies, such as precision agriculture, remote sensing, and machine learning, to support agricultural decision-making. However, the current state of agricultural meteorology is not equipped to meet the demands of CRA. The discipline faces several challenges, including a lack of interdisciplinary approaches, inadequate emphasis on practical skills, and limited focus on climate change and sustainability (Fig. 8.1). To address these challenges, essential reforms are necessary to reorient agricultural meteorology as a super speciality discipline. This chapter will discuss the essential reforms required to transform agricultural meteorology, including reforms in curriculum, research, practice, and policy. By implementing these

To set up a soil-testing laboratory the minimum requirements are as follows:

8.1 Laboratory floor area

Minimum four rooms are needed. They are as follows:

1. Office room, floor size: 18’x 12’

This room is used for registration of the soil samples received, recording recommendations along with soil test results in laboratory book and in report sheet, their dispatch, accounts etc.

[Bray’s Method (Bray and Kurtz, 1945) – Acid Soils]

Introduction

The term available phosphorus (P) refers to the inorganic form, occurring in the soil solution, which is almost exclusively ‘orthophosphate’. This orthophosphate occurs in several forms and combinations, and only a small fraction of the total amount present may be available to plants, which is of direct relevance in assessing the P fertility level.

The phosphate concentration in solution is governed by heterogeneous equilibria in which it takes part. This situation can be represented as follows:

P adsorbed in solid phase ↔ P in the soil solution ↔ P precipitated

The phosphorus absorbed by plants from soil solution comes from the soil solution in which it exists as inorganic orthophosphate ions viz., H₂PO₄ ^–, HPO₄ ^2– and PO₄ ^3–. The most accessible ion is H₂PO₄ ^–, with the greatest activity coefficient, followed by HPO₄ ^2–. The quantity of P accessible to the plants is influenced by a series of soil properties. The relative abundance of these ions is, however, relatively dependent on the soil pH. For soils having a pH between 4.5 and 7.5, ions of H₂PO₄ ^– as well as HPO₄ ^2– exist in soil solution. At a pH of 7.2, H₂PO₄ ^–and HPO₄ ^2– ions have an equal activity, and, when the pH is strongly alkaline (>8.3), ions of HPO₄ ^2– predominate in solution. Above pH of 9.0, the trivalent ion (PO₄ ^3–) becomes more important than H₂PO₄ ^–, but even at a pH of 12, the HPO₄ ^2– concentration is still greater than that of PO₄ ^3–. Soluble phosphorus may be adsorptively retained at the surface of colloidal particles. This retention is more marked when the higher amounts of clay and sesquioxides are present. Relatively, the available inorganic P tends to accumulate in its most stable state under prevailing conditions, thus, in calcareous soils, the available inorganic P would be acid-soluble, whereas, in acid soils the adsorbed P would be more available. In flooded soils, with low oxidation potential, certain forms of P, normally considered as unavailable, for example, iron and phosphate, can be regarded as available. In most soils, the main source of orthophosphate is organic matter unless, of course, direct fertilization, with soluble phosphate, has been made.

(Neutral Normal Ammonium Acetate Method)

Introduction

The total potassium (K) content of a soil varies from 0.05 to 2.5 %. The total K is distributed in mineral form (lattice – K, 90-98 %), fixed non-exchangeable or temporarily retrograded K (1-10 %) and exchangeable plus water soluble K (1-2 %). Exchangeable K represents an average of 2 – 15 % of the sum of exchangeable bases and 1 – 3 % of the total capacity for cation exchange. The K concentration in the soil solution is on an average 0.08 – 3 meq/litre. Both water soluble and exchangeable K are most accessible to the plant. Available K can, thus be separated into that, immediately available, which is water soluble and exchangeable, and that, potentially available or fixed. The neutral normal ammonium acetate extract contains both water soluble and exchangeable K. K extraction by this extractant is considered as a suitable index of K availability in most soils, based on crop response correlation study. Thus, the K extracted by this method is equated as the available K.

[Williams and Steinbergs (1959) Method]

Introduction

Sulphur (S) occurs in numerous forms in soil, viz., sulphites, sulphates, sulphides and in organic compounds. It is considered that the most accessible form is ‘sulphate’ (SO₄ ^2–). The organic forms of S compounds become assimilable, especially following, microbiological transformation into SO₄ ^2–. The mineralization of organic S in a soil depends primarily upon the N:S ratio, and any SO₄ ^2– formed may be fixed against extraction, particularly if much Fe or Ba is present or if the soil is very acid. Despite the fact that the plants absorb S almost exclusively as SO₄ ^2–, mobility of SO₄ ^2– in soil may not always yield satisfactory results in accordance with the time of sampling, while assessing SO₄ ^2– availability.

The Electrical Conductivity (EC) of the soil is a measure of soluble salts present in the soil and is expressed as millimhos/cm or dSm^-1.

Principle

As the amount of the soluble salts in a solution increases, the electrical conductivity also increases. This electrical conductivity is measured in terms of resistance offered to the flow of current using a conductivity bridge.

It is known that solutions offer some resistance to the passage of current through them, depending upon the concentration of the soluble salts present. Hence, EC is measured in terms of electrical Resistance between parallel electrodes immersed in the soil water suspension.

In such a system, the solution between the electrodes becomes the electrical conductor to which the physical laws relating to resistance are applicable. The electrical resistance ‘R’ is directly proportional to the distance ‘L’ between the electrodes and inversely proportional to the cross sectional area ‘A’ of the conductor.

[Titrimetric/ Walkley and Black (1934) Method]

Introduction

The role of soil organic matter, in relation to soil fertility and physical conditions, are widely recognized. The organic matter is the source of plant nutrients which are released inassimilable forms during microbial degradation. A major proportion of N (95 – 99 % of the total), P (33 – 67 % of the total) and S (75 % of the total) in soils occur in organic combinations, which mineralize to release the nutrients in inorganic forms to be used by plants. Nevertheless, it serves as a reservoir of plant nutrients, in promoting water storage, and in regulating microbial activity.

The organic matter content of a soil varies from 0.344 % in very sandy arid soils to more than 86 % in Peats and Mucks. Soil organic matter contains 5 % N, and 0.5 % each of P and S, thereby, having a N:P:S ratio of 10:1:1. The organic matter content of a soil is estimated by the amount of organic carbon (C) present, as this element represents, on an average, 52 – 58 % of the organic matter, and the C:N ratio is 10 – 15. In the sub-soil, the average organic carbon amount is 36 – 44 % (40 %), and the C:N ratio is < 8. In order to find out the amount of humified organic material, the organic carbon content is multiplied by 1.724, which represents the ratio between the humus and the organic carbon (100/58) and is known as the ‘von Bemmlen factor ’.

The organic matter content of soils is estimated from the organic carbon, determined by using the ‘Titrimetric method / Walkley and Black method. The Titrimetric determination, also known as the ‘wet-digestion method’ involves a rapid titration procedure for the estimation of organic carbon content of soils.

The pH is defined as negative logarithm of hydrogen ion concentration or the log of the reciprocal of the hydrogen ion concentration (Sorenson, 1906).

Principle

A glass electrode in contact with hydrogen ions of the solution acquires an electric potential which depends on the concentration of hydrogen ions. This is measured potentiometrically against some reference electrode which is usually a calomel electrode. The potential difference between glass electrode and calomel electrode is expressed in pH units.

Two electrodes are used in the determination of pH. One is reference electrode which provides a standard voltage and is usually a saturated calomel electrode which has two layers of saturated solution of KCl and mixture of solid HgCl₂ and Hg. The outer tube is usually 5-15 cm long, 0.5-1 cm in diameter. The mixture of solid HgCl₂ + Hg paste is contained in an inner tube that is connected to the saturated KCl solution in the outer tube by means of small opening. The resistance developed by this type of electrode is 2000-3000 Ohms.

The outer electrode is glass electrode that consists of a tube enclosing a lead wire made up of Ag coated with AgCl. This wire is again enclosed in a wax insulation. To the tube at the bottom is attached a glass bulb made up of a special kind of glass which is sensitive to hydrogen ions. The thickness of the glass membrane varies from 0.03 to 0.1 mm and has a resistance of 50 to 500 mega Ohms.

When these electrodes are dipped in solution, the saturated solution of KCl comes out of reference through small holes and forms an invisible ionic bridge between electrodes through which current passes. The hydrogen ions are absorbed by glass electrode and depending upon the amount of hydrogen ions present in the solution, an electric potential develops between electrodes. This potential difference is measured in terms of pH units by suitable Galvanometer.

Objective
To produce ethanol from lignocellulosic biomass (e.g., paddy straw)
through a series of biochemical processes pretreatment, enzymatic saccharification, and microbial fermentation and to evaluate sugar yield, ethanol concentration, and fermentation efficiency.
Introduction and Theoretical Background
Ethanol (C2H5OH), commonly known as ethyl alcohol, is one of the most important biofuels and renewable energy sources in the modern era. It is a clear, volatile, and flammable liquid that can be used both as a fuel additive (blended with gasoline to form gasohol) and as an independent energy source in modified internal combustion engines. In addition, ethanol is extensively used in the pharmaceutical, beverage, and chemical industries. Globally, more than 30 billion litres of ethanol are produced annually, primarily via fermentation of sugar-rich feedstocks such as sugarcane juice, molasses, and starch-based crops like maize and wheat. However, reliance on these food-based resources has raised sustainability and food security concerns. Therefore, attention has increasingly shifted toward lignocellulosic biomass, such as agricultural residues (paddy straw, corn stover, bagasse, etc.), which are abundant, renewable, and inexpensive.
Paddy straw, a by-product of rice cultivation, is a promising raw material for bioethanol production due to its high cellulose (30–40%) and hemicellulose (20–30%) content. However, the complex structure of lignocellulosic biomass, reinforced by lignin and silica, restricts enzyme accessibility to polysaccharides. Hence, pretreatment is essential to break down lignin and enhance enzyme digestibility before fermentation.

13.1 Introduction

Eutrophication, means an increase in chemical nutrients — typically compounds containing nitrogen and phosphorus in an ecosystem. It may occur on land or in water. The term is however often used to mean the resultant increase in the ecosystem’s primary productivity (Fig. 13.1), in other words excessive plant growth and decay and even further impacts, including lack of oxygen and severe reductions in water quality and in fish and other animal populations.

Abstract

The paper presents an approach of selecting suitable areas for artificial groundwater recharge structures through remote sensing and integrated terrain analysis by statistical method. The study area “Ayyar basin” is an aquifer system of gneisses and charnockite litho-units in Tiruchirappalli district, Tamil Nadu. The area has witnessed a steady decline in groundwater table. Hence, the scope for artificial groundwater recharge was studied in this area with the help of Tamil Nadu State Council for Science and Technology (TNSCS&T).