28 Mar, 2024
Precision Farming Technologies towards Enhancing Productivity and Sustainability of Cereal Based Cropping System
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Introduction

Precision farming or precision agriculture is generally defined as information and technologybased farm management system to identify, analyse, manage spatial and temporal variability within fields for optimum productivity, profitability. Sustainability and protection of the land resource by minimizing the production costs. Increasing environmental consciousness of the general public is necessitating us to modify agricultural management practices for sustainable conservation of natural resources such as water, air and soil quality while staying economically profitable. The use of inputs viz., chemical, fertilizers and pesticides based on the right quantity at the right time and in right place. This type of management is commonly known as “Site-Specific Management”. The productivity gain in global food supply have increasingly relied on expansion of irrigation schemes over recent decades with more than a third of the world’s food now requiring irrigation for production. All together market based global production in agricultural products is challenging economic viability of the traditional agricultural systems and requires the development of new and dynamic production systems. Precision farming is an approach where inputs are utilized in precise amounts to get increased average yields compared to traditional cultivation techniques. The precision farming developments of today can provide the technology for the environment friendly agriculture of tomorrow. Especially in the case of small farmers in developing countries, precision farming holds the promise of substantial yield improvement with minimal external input use

Need of Precision Farming

The global food system faces formidable challenges today that will increase markedly over the next 40 years. Much can be achieved immediately with current technologies and knowledge, given sufficient will and investment. But coping with future challenges will require more radical changes to the food system and investment in research to provide new solutions to novel problems. The decline in the total productivity, diminishing and degrading natural resources, stagnating farm incomes, lack of eco-regional approach, declining and fragmented land holdings, trade liberalization on agriculture, limited employment opportunities in non-farm sector, and global climatic variation have become major concerns in agricultural growth and development. These variations can be traced to management practices, soil properties and/or environmental characteristics. The level of knowledge of field conditions is difficult to maintain because of the large sizes and changes due to annual shifts in leasing arrangements in the farm area. So the entire farm area has to be divided into small farm units of 50 cents or less. Precision agriculture offers the potential to automate and simplify the collection and analysis of information. It allows management decisions to be made and quickly implemented on small areas within larger fields.

Components of precision farming

Geographical information system (GIS), Global positioning system (GPS), Remote sensing (RS) and the farmer are the major components of precision farming.

Geographical information system (GIS): The precision farming is information based technology that concerned with spatial and temporal variability wherein GIS is the key to extracting value from information on variability. GIS is the brain of precision farming system and spatial analysis

capabilities of GIS that enable precision farming.

Global positioning system (GPS): All phases of precision agriculture require positioning information and it can be provided by the GPS (developed by the US military) in an efficient manner. The GPS provides the accurate positional information which is useful in locating the spatial variability with accuracy. The GPS can be used in two modes; single receiver mode and differential mode (DGPS) using two receivers. Single receiver collects the timing information and processes it into position. In the differential mode (DGPS), one receiver is mounted in a stationary position; usually at farm office while the other is on the machine/implement.

Remote sensing: Remote sensing has been used in soil mapping, terrain analysis, crop stress, yield mapping and estimation of soil organic matter, but on a scale larger than what is required for precision agriculture. Remote sensing at high resolution can be of great use in precision farming because of its capacity to monitor the spatial variability. The remote sensing satellites send a known signal towards the earth and portion of the signal is reflected back. The image data are actively collected by measuring these signals. The image data are actively collected by measuring these signals. Data are also collected passively by measuring the sun’s energy reflected by an object or electromagnetic energy emanated from an object. Remote sensing can be of various resolution, spectral coverage and frequency. In precision farming different applications will require different spatial resolutions, spectral coverage and frequencies. For eg., measurement of the intensity of disease infestation will require higher resolutions than what required for crop growth monitoring or yield mapping.

Farmer: As farming cannot be imagined without farmer, the latter must be considered as an essential component of precision farming too. For assessing and managing the variability, decision-making is the key factor, and it is to be done in consultation with the farmer. In a wider perspective, it can be visualized that farmers do practice precision farming to some extent as they make variable rate application of inputs in their different piece of fields. But, the decision is based on variability what they perceive rather than the real variability that exists. Precision farming is information and knowledge based farming. Therefore, farmer shave to be trained for practicing precision agriculture, and convinced to accept the same. Further, the resource endowment and socio-economic condition of the farmer have to betaken into consideration before prescribing any tool or step of precision farming.

 

Approaches for precision farming

In precision farming, inputs are to be applied precisely in accordance with the existing variability. Therefore, assessing the in-field variability soil and crop is very crucial and first step of precision agriculture. Spatial variability of all the determinants of crop yield (topography, soil properties etc) should be well recognized, adequately quantified and properly located. Construction of condition maps on the basis of the variability is a critical component of precision farming. Condition maps can be generated through (i)Surveys, (ii) Point sampling and interpolation, (iii) Remote sensing (high resolution) and (iv)Modeling.

Precision planting

Seed is becoming a costly input and its placement at desired place and in required amount needs special attention. Manual sowing / planting is still a common practice in India, which has to do away with low cost precision planters provided with précised seed metering devices to ensure optimum plant population at lesser seed rates. Design and development of new generation multi-crop planters with precise seed metering systems are a step forward in the precision planting. A

handful of reports on effect of these technologies revealed that the water productivity of the  rice-wheat cropping system improved significantly though saving on irrigation water and/or improvement in crop yields. Better crop establishment and higher crop yields with less seed rates using these precision planters particularly in high value crops.

 

Site-specific nutrient management

In India, fertilizers are generally broad caste don the basis of so-called recommended doses. As these recommendations are made for either national or state basis, they are always erroneous for a particular field. Site-specific nutrient management (SSNM), a general concept for optimizing the supply and demand of nutrients according to their variation in time and space, is being tried in India for achieving the high yield targets. 

Real time nitrogen management using leaf color chart (LCC) and SPAD meter

Diagnostic surveys on nutrient management practices prevailing in rice in Indo-Gangetic plain Region of India revealed that nearly one-third of rice farmers apply as much as 180 kg fertilizer N/ ha which is much higher than the recommended levels. Irrational N applications not only lead to less N recovery efficiency (< 50 %), crop losses due to lodging, higher cost of production but also to nitrate leaching and pollution in ground water. The simple, handy and user friendly tools like leaf color charts (LCC) have been evaluated and are being used for real time N application. For real time N application in rice-wheat cropping system, The LCC and SPAD (chlorophyll meter) were calibrated and it was observed that there was a strong correlation (r= 0.84 to 0.91) in different genotypes of rice and wheat. The regression analysis showed a significant relationship between LCC and SPAD value at all growth stages for all cultivars of rice and wheat. They further reported an increase of 19 to 31 % in net returns with LCC based N application than the fixed time N application. The LCC can serve as the farmer friendly tool for precision N management. Results of farmer participatory field trials in western UP on N management in Basmati rice (PB-1) revealed that rice grain yield and agronomic efficiency of N responded significantly positive to LCC based N application (Jat and Sharma, 2017). The highest rice yield was obtained under fertilizer N management using LCC < 3, which out yielded to farmer, N management practice (100 kg N/ ha) in 3 splits) by 12 %. As compared to LCC < 3 the additional application of 24 kg N/ hain 80 % N as basal + LCC < 3 treated plot also did not resulted in higher grain yield, hence agronomic efficiency got decreased by 15.5 %. Lower yield under farmer’s managed plot/ 80 %basal N + LCC < 3 treatment even after application of 20 to 24 kg ha-1 extra N was due to crop lodging associated with unfavorable weather conditions. By using the criteria of LCC < 3 with 20 kg N/ ha per application has resulted in a total of 80 kg N/ha. Although, this amount was equivalent to fixed time recommended N splits, it produced higher grain yield and agronomic efficiency. The increase in grain yield and agronomic efficiency were 8.3 and 28.8 %, respectively.

Precision nitrogen management using optical sensors (Green Seeker)

Spatially variable nature of the plant available nitrogen in agro-ecosystems, crop growth and fertilizer N requirement vary temporally among and within seasons and spatially among and within fields. Uniform application of nitrogen fertilizer can lead to some areas of a given field receiving excessive nitrogen fertilizer while other areas are left under fertilized. Fertilizer N management that does not accommodate temporal and spatial variability may lead to suboptimal yields and net returns, poor N use efficiency and escape to the environment of excess fertilizer nitrogen. Thus, quantifying the optimum in-season N requirement is an important step towards economically and environmentally viable crop production systems. Application of nitrogen fertilizer in a spatially variable manner that corresponds to the spatial variability of the nitrogen needs of the crop could lead to increased nitrogen use efficiency and thus more economically and environmentally sound farming practices. Farmers generally use blanket application of higher amount of fertilizer nitrogen irrespective of the soil supplying capacity and crop demand that leads to losses of N as NO3- leaching and NH4+volatilization, higher cost of production and lower fertilizer N use efficiency. Assessing in-field variability of plant N status by collecting in-season biomass samples is cost prohibitive, labor intensive and destructive to the crop. Remote sensing is more rapid means to sample multiple crop parameters including photosynthetic capacity, productivity and potential yield. Spectral vegetation indices such as the normalized difference vegetation index (NDVI) have been shown to be useful for indirectly obtaining crop information such as photosynthetic efficiency, productivity potential and potential yield. NDVI is a broadband index that is well correlated to leaf area index and green biomass and is thus sensitive to photosynthetic efficiency. Green Seeker hand held optical sensor is on the go remote sensing and measures the reflectance of a given crop area over a 0.61 x 0.61 m area when the unit is positioned between 0.6 and 1.0 m above the target area (Singh et al.,2020). 


Conclusion:

Precision farming is still only a concept in many developing countries and strategic support from the public and private sectors is essential to promote its rapid adoption. Successful adoption, however, comprises at least three phases including exploration, analysis and execution. Precision agriculture can address both economic and environmental issues that surround production agriculture today. Questions remain about cost-effectiveness and the most effective ways to use the technological tools we now have, but the concept of “doing the right thing in the right place at the right time” has a strong intuitive appeal. In the light of today’s urgent need, there should be an all-out effort to use new technological inputs to make the ‘Green Revolution’ as an ‘Evergreen Revolution’. Ultimately, the success of precision agriculture depends largely on how well and how quickly the knowledge needed to guide the new technologies can be found.  Precision farming provides a new solution using a systems approach for today’s agricultural issues such as the need to balance productivity with environmental concerns. It is based on advanced information technology. It includes describing and modelling variation in soils and plant species, and integrating agricultural practices to meet site-specific requirements. It aims at increased economic returns, as well as at reducing the energy input and the environmental impact of agriculture.

Dr Neha Dahiya, Assistant Professor

Dr Neha Dahiya, Assistant Professor

School of Agricultural Studies, Geeta University, Panipat, Haryana

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