Agricultural land use is always expected to supply the nation with enough quantity
food. Thus, intensification is necessitated in agriculture. Continuous intensive
agriculture without adequate attention on rational soil management poses serious
threat to the sustainability of agro-ecosystems. One of the consequences of
irrational soil management under intensive cropping is the decline in soil fertility,
i.e., the levels of nutrient replenishments are low to redress the process of
nutrient depletion. This nutrient mining does not get the same public attention
as droughts, pest infestation etc., since it is a gradual process and not associated
with catastrophes and mass starvation and therefore largely invisible (Smaling,
1993). Also, pertinent is that many soil test methods do not readily reveal
nutrient mining because the available fraction extracted is buffered well by
supply from other nutrient pools, as is often seen with K availability in some
high CEC soils. Prognoses for the effect of soil nutrient depletion on future
agricultural production are even more difficult to establish.
The change in soil nutrient stocks over time has to be measured in order to
quantify the extent of nutrient mining and also to provide an early caveat on
adverse trends in nutrient inflows and outflows from the farm. Hence, a quantitative
knowledge on the depletion of plant nutrients from soil is essential to understand
the status of soil degradation and to devise optimum nutrient management strategies.
Such a quantitative knowledge forms the basis and is essential for any management
programme aimed to ensure sustainability in agro-production systems. Restoration
of soil fertility can no longer be regarded as an issue connected with the use
of organic and inorganic nutrient sources only. It requires a long-term perspective
and a holistic approach. The holistic approach should take care of the nutrient
stocks within a farm; their flow between various activities within the farm
and nutrient balance at farm level that is arrived by matching nutrient inflows
into the farm and nutrient exports out of the farm (Vlaming
et al., 2001).
|| Compound growth rate in yield for major crops
Such a knowledge intensive management plan requires participatory research
and development focus rather than a purely technical focus. The outcome from
it will be useful to agricultural policy makers and also to the farmers to design
policy interventions to mitigate undesirable trends, if any.
Crop yields harvested with the present level of fertilizer recommendations and the yields harvested in many fertilizer response experiments with higher levels than the present level have unequivocally revealed that the fertilizer optima calculated in present day research are erroneous and in many cases sub-optimal. Further, the boundary lines for the application of fertilizer management technologies is mostly administrative boundary (sometimes fewer parameter) based rather than science based and is the major cause for technology failures and degradation of resource base.
The compound growth rate in yield in major crops is either declining or negative (Table 1). It is the experience of the researchers that with newer crop varieties these yield barriers cannot be broken. The logical conclusion is that the soil resource base is degraded below a critical level that newer crop varieties or hybrids are not able to yield beyond a level, which is primarily determined by the level of native soil fertility. The amount of fertilizer used is also sub-optimal (and imbalanced too) at the site of application and therefore the expected yield increase is not realized.
This study envisages a wholesome approach in five phases covering all the above aspects in order to ensure real optimum character to these fertilizer optima and sustainability to agro-production systems that are vital to break the crop yield barrier.
Phase I: Farm level nutrient budgeting: Farm level nutrient budgeting is an essential component in any programme that aims towards sustainability in agriculture. It must be the base level information for farm or region or national level policies to promote INM (Integrated Nutrient Management) programmes. The first phase deals with the farm level nutrient budgeting using nutrient monitoring approach. Nutrient monitoring is a method that quantifies a systems nutrient inflows and outflows resulting in nutrient balance. Nutrient balance can be determined at spatial scales ranging from field level to continental scale. The nutrient balance studies at the level of individual activities within a farming system serve as a useful indicator and variable tool, providing insight into causes and magnitudes of losses of nutrients from the system and so helping to target interventions in places where undesirable trends are witnessed.
When this nutrient monitoring is applied to a system, where different management
practices are being introduced, it can be used to assess the effects of these
management practices on soil nutrient stocks and flows (Sharma,
2008; Stoorvogel et al., 1993; Smaling
et al., 1993). Larger scale estimates of nutrient balance and budgeting
have become increasingly influential in policy discussion related to soil fertility
management and sustainable agriculture (World Bank, 1996).
Nutrient monitoring has been a valuable tool for scientists to summarize and
facilitate the policy makers to understand the nutrient cycling in agroecosystems
and used as regulatory policy instruments (De Walle and Sevenstar,
Murugappan et al. (1999) have observed that
irrespective of whether K is added or not, mining of soil K occurred with continuous
intensive cropping due to a luxury consumption which would limit crop yields
due to severe K deficiency and render the land chemically degraded. Unchecked
nutrient depletion or accumulation has major implication on sustainability of
agricultural systems and future world food supplies (Syers,
1997). Nutrient deficits occur in many Sub-Saharan African countries (SSA)
(Stoorvogel and Smaling, 1990; Smaling,
1993) while nutrient surpluses are found in industrialized countries. Nutrient
budgets can be used as an agro-environmental indicator and awareness raiser
of nutrient (mis) use and nutrient use (in) efficiency (OECD,
2001). Nutrient balances/audits can provide an early indication of potential
problems arising from a nutrient surplus (inputs > outputs) leading to accumulation
of nutrients or a deficit (inputs < outputs) leading to depletion of soil
nutrient reserves there by increasing the risk of toxicities or deficiencies,
respectively, both of which reduce crop yields (Cuttle, 2002).
Approaches to nutrient budgeting: Budgets are the outcome of a simple
nutrient accounting process, which detail all the inputs and outputs to a given
or defined system over a fixed period of time. Recently many computerized decision
support models for working out the nutrient balance were developed, viz., NUTrient
MONitoring (NUTMON), NUtrient MAnagement Support System (NUMASS), MINeral Accounting
system (MINAS), N CYCLE, OVERSEER, Ythan Nutrient budgeting tool etc., of-which
NUTMON-Toolbox is a user friendly computerized software (developed by Wageningen
University, The Netherlands) for monitoring nutrient flows and stock especially
in tropical soils (Vlaming et al., 2001).
Farm level nutrient budgeting is done using this software based on nutrient stocks (where the nutrients are held in the farm) and flows (nutrient flow from one activity to another) with in a farm in order to assess whether the present farm practices ensure a positive nutrient balance or a negative one. In this phase the sustainability of the farm is assessed and the causes for the negative trends are identified which form the basis for making policy interventions to correct the negative trends.
Employing the computer software NUTMON-Toolbox and its calibration and validation enables quick assessment of nutrient balances at different scales, viz., farm, district and national levels. These nutrient balances are valuable information for farm management and policy makers. One is able to identify the source where the nutrients are held within a farm and their flows between different farm activities, viz., Primary Production Units (PPUs) (crops), Secondary Production Units (SPUs) (cattle, goat, poultry etc.) and Redistribution Units (RUs) (manure heap, crop). Thus the calibrated and validated NUTMON-Toolbox is a powerful tool in the hands of Scientists and Research Scholars who work in the field of INM to sustain crop yields and agro-production systems.
Structure and usefulness of NUTMON-Toolbox: The concept is based on
five nutrient inputs (mineral fertilizer, organic manure, atmospheric deposition,
biological nitrogen fixation, sedimentation) and five outputs (harvested products,
crop residues, leaching, gaseous losses and erosion) and basically scale-independent.
NUTMON is fed by a number of basic data and by nutrient input and output data.
Basic data include the hectarage of the arable land and the spatial patterns
of land use systems, i.e., the combination of prevailing soils and climate on
the one hand and cropping and livestock systems on the other hand. Nutrient
input and output data are reflections of the processes IN 1-5 and OUT 1-5. Each
process has certain value and the nutrient balance is given by Σ IN- Σ
To determine nutrient input (IN 1-5) and output (OUT 1-5) values, a step-wise approach was followed viz., calculation, estimation or assumption. Some steps relate to data that were easily measured or obtained from farmer itself, for example fertilizer type and quantity and also the yield and hectarage of crops, but others related to more complex processes, for example amount of crop residues per land use system and data on denitrification and leaching losses. Some data needed continuous recording, for example quantity and frequency of irrigation and wet atmospheric precipitation, if any, while others were required irregularly, for example physical properties of soil etc.
On-farm nutrient flow: ways and means: The nutrient input flows in the experimental farm were identified as mineral fertilizers (IN 1), on-farm and off-farm (purchased) manures (IN 2), atmospheric deposition (IN 3) and biological N fixation (IN 4). Nutrient output flows were identified as crop uptake (OUT 1), removal in crop residues (OUT 2), leaching (OUT 3) and gaseous (volatilization) loss (OUT 4) (Fig. 1).
|| Concept of On farm nutrient management and conservation of
|| NUTMON-Toolbox generated nutrient balance for the experimental
|| NUTMON -Toolbox generated nutrient balance for the irrigated
medium farm in Nathegoundenpudur
A major portion of the crop residues was returned to the farm as on-farm manure
through livestock feeding. A small portion was recycled directly into the farm.
A major portion of nutrients was exported out of the farm in harvested produces,
which were meant either for human consumption or sale. I t was also assumed
that considerable amount of N and a small amount of K were leached out of the
Presentation of nutrient budgets-case studies: Murugappan and Jagadeeswaran (2003) made an attempt for nutrient monitoring in a farm at Govanur village in Coimbatore district of South India, using NUTMON-Toolbox (NUTrient Monitoring Toolbox). The nutrient balance was expressed as the sum of inputs, (IN 1 + IN 2 + IN 3 + IN 4) minus the sum of outputs (OUT 1 + OUT 2 + OUT 3 + OUT 4), i.e., Σ IN-ΣOUT. The results indicated that N balance for the farm as a whole was positive (+15.3 kg) whereas these balances were negative for P (-9.6 kg) and K (-157.8 kg) (Table 2 ). It indicated that the usage of nitrogen fertilizers is sufficient enough to contribute for the crop uptake. The negative P and K balances might be attributed to the sub-optimal use of these fertilizers. Also, luxury consumption of K by the crops contributed to its negative balance.
Nutrient balance studies for the major nutrients viz., N, P and K were also
monitored at different spatial scales viz., field (crop activity), farm and
district levels in the Coimbatore and Erode districts of Western agro-climatic
zone of Tamil Nadu by employing NUTMON-Toolbox (Surendran,
2005; Surendran and Murugappan, 2007). Eight irrigated
and eight rainfed farms were considered in the study. The following are the
results at different special scales.
At crop activity level: The nutrient balances at individual crop activity (primary production units -PPUs) level in a irrigated farm located at Nathegoundenpudur, Coimbatore district are presented as a example in Table 3. The results indicated that the calculated N and K balances were negative for banana, turmeric and maize, whereas for most of the crops the P balance was positive.
Flow diagram representing the various units within this farm is given in Fig.
2. The flow of nutrients to the farm from external sources was mainly through
mineral fertilizers (IN 1) and on-farm and off-farm manures (IN 2) to banana
(PPU 2), onion + chillies (PPU 3) and turmeric (PPU 5). The volume of flow to
these PPUs was large as compared to the flows to other PPUs viz., pulses (PPU
6), maize (PPU 4) and sorghum (PPU 1). Similarly, the nutrients exported out
of the farm (EXT) from PPUs that occurred through the STOCK was large with maize
(PPU 4), turmeric (PPU 5), onion + chillies intercropping (PPU 3) and blackgram
|| Various units in the irrigated medium farm at Nathegoundenpudur
|| NUTMON Toolbox generated nutrient balances at farm level
In the case of banana (PPU 5) and sorghum (PPU 1) nutrients were directly exported
out of the farm (EXT) without going into the STOCK. The volume of flow of nutrients
exported (EXT) from the STOCK was the largest in the farm and occurred through
human consumption and sale of produce. Some quantity of nutrients has come into
the STOCK as seed materials and stored in the farm. Similarly some quantity
of nutrient flow occurred to the PPUs (viz., sorghum, onion and blackgram) from
stock as planting materials.
The Secondary Production Units (SPUs) in the farm are tropical lactating cow (SPU 1, SPU 2) from which, nutrient flow occurred mainly to turmeric (PPU 5) and banana (PPU 2) through the compost heap (Redistribution Unit-RU 1). Also, through a portion of the residue from onion + chillies intercropping (PPU 3) and from external source as mineral fertilizers (viz., urea and SSP) nutrient flow occurred to compost heap (RU 1).
At farm level: The mean farm level N balances for Coimbatore and Erode districts were negative for both irrigated and rainfed situations (Table 4) (-1.8 and -0.7 kg/ha/year, respectively). The P balance was positive for irrigated farms (9.4 kg/ha/year) and negative for rainfed farms (-2.5 kg/ha/year). The average K balance was most negative in both irrigated and rainfed conditions (-46.7 and -13.1 kg/ha/year, respectively). The intensity of negative balance was high in Coimbatore district as compared to Erode district (-10.2, 1.4 and -36.3 kg N, P and K/ha/year in Coimbatore district and 7.8, 5.4 and -23.4 kg N, P and K/ha/year in Erode district).
|| Nutrient balance at district scale for Western zone of Tamil
The observed N balances was highly positive (62.0 kg ha-1) in a
medium farm and highly negative (-48.6 kg ha-1) in a marginal farm.
P balance was positive in almost all the selected irrigated farms. The positive
balance was of high magnitude in a medium farm (37.1 kg ha-1). Full
and partial K balances were negative at all farms irrespective of the farm size.
The highest negative balance of K was noticed in a marginal farm (-126.7 kg
ha-1). In rainfed farms most of the crops exhibited a negative N,
P and K balances and this negative balance was of high magnitude with hedge
lucerne, fodder sorghum, sorghum etc. P and K balances were found to be negative
in all the selected rainfed farms of both the districts. K balance was found
to be highly negative (-57.0 kg ha-1) in a medium farm (Table
These results indicated the need for critically reviewing the existing fertilizer recommendations for agricultural crops in general and sugarcane in particular. Other possible management to mitigate negative nutrient balances range from nutrient addition technologies (i.e., nutrient additions through mineral or organic sources, crop rotation etc.) to nutrient saving technologies (i.e., practices that aim to increase nutrient use efficiency viz., time and method of application, use of slow/controlled release fertilizers, use of urease/ nitrification inhibitors, use of N fixing and P solubilizing bio-fertilizers etc.). However, one single technology cannot solve nutrient related problems and hence solutions have to be sought from basket of technology options that should be economically feasible for the targeted farmers.
Nutrient balance at district level: At district scale the per hectare
N and K balances were negative (-10.1 and -3.3 kg N/ha/year and -9.8 and -58.6
kg K/ha/year, respectively for Coimbatore and Erode districts), whereas P exhibited
a positive balance in both cases (20.5 and 14.5 kg/ha/yea, respectively) (Table
5). The positive balance of P is the result the accumulation of P over years
due to P fertilizer application and also the losses were low since the soils
in the study area tends to fix P. The positive balance of P will result in an
increased risk of nutrient emissions to the environment causing nutrient toxicity.
The enhancement of P in soil reserves may lead to the contamination of surface
and ground water causing accelerated eutrophication and poses risks of toxicity
to aquatic life (CAST, 1996).
The negative balance of N and K implies that a net depletion of these nutrients
from the soil reserves occurs. N is mobile in the soil system and is also lost
from the system by leaching, volatilization of NH3 in soils whose
pH is more than neutral and denitrification in soils where submergence is a
practice. All the three channels operate in the study area. While comparing
the districts it was found that the high negative K balance in Erode district
is mainly due to existing cropping system in which K depleting crops like turmeric,
tapioca, banana, maize and paddy are grown. In the case of K, removal of harvested
product (OUT 1 and 2) proved to be the strongest negative contributor followed
by leaching which occurs in the study area since the soil characteristics are
conducive for leaching. Yet the wider negative balance obtained may be due to
suboptimal use of inputs in the study area. Continued nutrient mining process
goes at the expense of soil nutrient from the mineral and organic matter reserves
limits the crop yield and renders the land chemically degraded (Surendran
and Murugappan, 2006; Murugappan, 2000). We can undoubtedly
infer that the current practice of cropping system and nutrient management are
exhaustive in terms of N and K withdrawals and cause greater drain of these
nutrients from soil reserves. This process unchecked might lead to an irreversible
loss of soil fertility and eventually jeopardize the production in the years
to come and leaving the soils unfertile for the posterity. Declining soil fertility
also prevents income generation of the rural community and triggers the migration
of the rural population into urban centers in search of income and food at the
expense of social security. A nutrient audit model described in this study can
effectively play a role in assessing the problems and helps developing strategies
and practices that can be used to make useful policy interventions.
Phase II: Estimation of realistic fertilizer optima: In any fertilizer optimization research, unknown or surprise nutrient deficiencies other than those nutrients studied are not identified and removed before conducting field experiments. This is the major source of variation that imparts sub-optimal character to the recommendations. In a multi-step systematic approach in the first step, the existence of such nutrient deficiencies is identified in any benchmark soil for which the fertilizer optima is calculated based on nutrient sorption curves. In the second step, by a green house nutrient survey study the existence of these deficiencies is confirmed by quantitative measurements on biomass produced by sorghum seedlings grown under corrected and uncorrected situations. In the third step, optimum nutrient treatments are described based on the results of steps 1 and 2 and the field experiment with the test crop is conducted. Based on the response data, fertilizer optima are calculated and fertilizer calibrations are constructed and in the fourth step, these are validated in multi-location on-farm trials.
Case study: Using the systematic approach, Murugappan
and Jagadeeswaran (2003) optimized the fertilizer requirement for rice.
Nutrient sorption studies were performed in two soil types viz., Kalathur and
Irugur soil series for P, K, S, Zn, Cu, Mn and B. Using these sorption curves
and critical levels of nutrients, an Optimum Nutrient Treatment (ONT) was defined
for both the soil types (Table 6). These ONT was validated
using a green house experiment. The results indicate that the two experimental
soils were found to be deficient in N, P, K and Zn. In the third phase, a treatment
structure was formulated based on the ONT. In this structure the ONT formed
the central treatment. In each case of N, P and K there were three more levels
viz., a zero level, one below and one above the ONT. The grain yield data from
the field experiment exhibited that there was significant response for all the
nutrient studied. Fertilizer optimization was done for the rice response data
using Quadratic Polynomial Surface (QPS), Mitscherlich-Bray (M and B) function
and fertilizer prescription function for specified yield target (FP). These
standardized fertilizer amounts by different approaches were compared with blanket
fertilizer recommendation (Table 7). The results revealed
that the grain yields obtained were markedly higher than that of the existing
blanket recommendation. The N, P and K optima for rice by different approaches
were greater than the blanket recommendation. This implies that the existing
recommendations were old and evolved probably during late sixties or early seventies
with the advent of Green Revolution.
|| Optimum Nutrient Treatment (ONT) for the experimental soils
||Fertilizer amounts (kg ha-1 ) for rice by different
approaches in comparison to blanket recommendation
Though many of these recommendations for agricultural crops have been updated
later by field research, these were not given effect for adoption leaving the
older ones to persist which are sub-optimal in the present situation of changed
crop varieties planted on depleted soil resource.
Since, the nutrient balance studies in Coimbatore and Erode districts of Tamil Nadu proved beyond doubt that the nutrient balance tends to be negative at crop activity, farm and district scales, to ensure balanced fertilization, redefining of the existing fertilizer recommendation is necessary. This systematic approach is a valid procedure for calculating fertilizer optima because unknown nutrient deficiencies are identified and eliminated pre-hand in a systematic way before the conduct of field experiments that are meant for calculating fertilizer optima for a crop and a benchmark soil. Fertilizer calibrations constructed using the data of field experiments in this approach showed high validity. This is a foolproof method that is now available with the researchers who are working in the area of fertilizer optimization. This procedure must form part of soil fertility evaluation research in any country.
Phase III. Construction of a Decision Support System (DSS): The DSSIFER
(Decision Support System for Integrated Fertilizer Recommendation) is a agricultural
decision-making software, which helps to generate site specific balanced fertilizer
recommendations for almost all crops grown in Tamil Nadu. Based on the validated
fertilizer optima and fertilizer calibrations from the studies conducted by
employing the systematic approach a computerized DSSIFER was constructed as
a tool for promoting balanced fertilization (Fig. 3).
|| Screen capture of DSSIFER
Besides, fertilizer calibrations available with Tamil Nadu agricultural University
and the Department of Agriculture were also included as the basis for calculating
fertilizer amounts for crops that are not covered in the studies with systematic
approach. DSSIFER is a model product for transfer of technology and it aims
to increase fertilizer use efficiency and maintain soil fertility for sustainable
crop production. DSSIFER, a user friendly computerized product, is useful in
decision making in soil fertility management (Murugappan,
With soil available macro and micronutrient levels, which are the input data for generating site-specific fertilizer recommendation, this software verifies the availability of soil test calibration for that site specific situation from among the 28 site-specific situations for which fertilizer prescription functions for specific yield target are already stored in the database. If available, the software asks for a yield target and calculates the fertilizer requirements (for each site specific situation a predetermined range of yield target within which the soil test calibration will safely work is also provided in the database). If not available, the software then checks for the site specific situation from among Mitscherlich-Bray functions stored in the database for 24 situations and calculates N, P and K recommendations, respectively, for 87.5, 93.75 and 93.75% yield sufficiency. If the situation for which fertilizer recommendation is to be generated is not available in both the above data base, then the software generates recommendation for this situation from general blanket recommendation as 25% extra if the soil analyzes low or 25% less if the soil analyzes high or the blanket amount itself if the soil analyzes medium nutrient availability status.
The DSSIFER uses critical level of Zn, Cu, Mn, Fe and B as the database to compute micronutrients recommendation for the crops. If the soil analyzes below the critical limit then the software generates recommendation on crop needs of micronutrient fertilizers. In both cases of macro and micronutrient recommendations, besides the fertilizer amounts, the time and method of application is also printed in the output.
Besides calculating the fertilizer requirements, DSSIFER software also generates recommendations on saline alkali soil reclamation using the soil analysis input of pH and EC. Also from the irrigation water analysis it checks its quality and gives out its suitability for irrigation in the output with recommendation for the safe use of poor quality water.
Phase IV: Frequent review and updating of DSSIFER: The presently followed
fertilizer recommendations are mostly based on the outcome of research conducted
during initial years of Green Revolution and therefore they are obsolete. Recommendations,
which have emanated from most of the recent research, have not been officially
||Nutrient balances at farm and district level after adopting
DSSIFER generated integrated nutrient management strategy
Through an Interactive Workshop to Re-look into Fertilizer Recommendation in
Tamil Nadu these valuable research results were documented (Murugappan,
2004). This document Revised Fertilizer Recommendations for Crops in Tamil
Nadu was presented in the Annual Scientific Workshop 2004 and with its approval
released officially for adoption by farmers.
Assessment of the performance of DSSIFER software: Nutrient balances were computed using the NUTMON-Toolbox by replacing the farmers practice of fertilization with DSSIFER generated crop and site-specific fertilizer recommendations and the farmers crop yield with the yield targets fixed in DSSIFER. The results revealed that the nutrient balances were positive at both farm and district levels. The mean N, P and K balances were 29.6, 14.7 and 10.4 kg/ha/year at farm level and 41.8, 55.1 and 19.9 kg/ha/year at district level (Table 8). Thus, fertilizer programme generated by DSSIFER is not only balanced but also ensures sustainability of the agro-production systems.
Phase V: Defining recommendation domains to increase input use efficiency: Agro-ecological zoning within the major agro-climatic zones, which are the presently followed recommendation domains, is an essential component in attempts to increase fertilizer use efficiency. Soils response to management within an agro-ecological zone is more uniform as compared that of an agro-climatic zone because only climatic parameters are used to earmark boundaries for the latter. Superimposing soil fertility information (as fertility capability classes -FCC), Length of Growing Period (LGP) and moisture index as layers on a digitized map of a region on a GIS platform provides agro-ecological units each of which is uniform with respect to response to fertilizer management. This would also help to increase the use efficiency of fertilizers. This zoning was done for the Western Agro-climatic Zone of Tamil Nadu and linked to DSSIFER to ensure spatial application. Database on Village Level Fertility Indices (VLFIs) that are available with the Tamil Nadu Soil Testing Service was created in DSSIFER and DSSIFER is now so designed that by a click of a village name on the digitized map fertilizer recommendation specific to the village will be generated based on VLFI.
Defining and evaluation of agro-ecological units as a basis for nutrient management
and fertilizer recommendations were done in Western zone of Tamil Nadu (Murugappan,
2004). The taluk wise soil maps of the study area were digitized and the
district, taluk and village boundaries were superimposed over it in a GIS platform.
Irugur (15%) and Palladam (14%) soil series dominated the study area. Based
on the surface and subsurface texture, 6 Fertility Capability Classes (FCC)
were delineated. The study area was dominated by the FCC class Loam over loam
(74%). Utilizing the taluk wise historical weather data, the two districts were
classified into 4 Moisture Index (MI) zones in which the major area was under
C1 Dry Sub Humid (70%). The study area was found to have four LGP (Length of
Growing Period) zones among which two were dominant viz., 60 to 90 days (52%)
and 90 to 120 days (40%).
The Agro-ecological sub-zones were derived based on criteria that appear to have direct influence on the interactions of applied inputs and closely related fertility management practices, viz., soil texture, soil moisture balance, rainfall and PET (Potential Evapo-Transpiration). The FCC, MI and LGP maps were superimposed to derive 21 Agro-Ecological sub-zones in the study area. Among them, the first six zones occupy about 80% of the study area. Baring the 5.5% area occupied by forests/hill/habitation, the rest of the 74.5% of area belong to loam over loam FCC unit (Fig. 4).
Environmental benefits of this approach: On-farm nutrient budget reveals the state of art of farm management and helps to evolve environmentally sound policy interventions to mitigate negative trends in nutrient budgets.
Fertilizer optima evolved by Systematic Approach are realistic and of ground water and eutrophication of water bodies by P are eliminated. Besides, soil degradation due to K mining is prevented (sub-optimal fertilization induces K mining).
The DSSIFER based fertilization ensures balanced nutrient use and thereby the
risk of soil degradation due to nutrient mining and fertility depletion is prevented.
The risk of NO3¯ leaching to ground water, eutrophication of
water bodies by P are eliminated and soil degradation due to K mining is prevented.
|| (a) Soil, (b) FCC, (c) LGP, (d) MI maps and (e) agro ecological
zones map of Coimbatore and Erode district
Nutrient monitoring allows study of a farm in an integrative, holistic way, capturing a real and complex farm situation, in due consideration of the effects of different activities on the farm, on the nutrient stocks and flows and the economic performance of the farm. Analytical tools, such as the Nutrient Audit models/NUTMON-Toolbox can play a pivotal role in assessing the problem and paves way to develop sustainable nutrient management policies, strategies and practices. However, nutrient balance as such is an incomplete indicator for sustainability and interpretation of a balance is difficult because it is not directly related to soil nutrient stocks. Hence NUTMON-Toolbox coupled with the systems approach for fertilizer optimization, DSSIFER and Agro ecological zoning would serve the purpose of gaining insight into long-term effects of farm management on enhancing soil fertility and productivity.