International Conference on Agricultural and Food Engineering (Cafei2016)

23-25 August 2016

e-ISBN 978-967-960-401-6 277

CAFEi2016-178

Variable rate application of fertilizer in rice precision farming

A.R. Mohd Syaifudin1,a, M.G. Mohd Sharil Shah1, C.C. Teoh1, B. Mohamad Aufa1, N. Mohd

Nadzim1, F.Z. Fakrul Radzi1, M.Y. Mohamad Najib2, C.S. Fairol Zamzuri3, D. Abu Hassan4, and

A.S. Mohd Haffiez1

1Engineering Research Centre, MARDI Headquarters, Serdang, Selangor. Malaysia; 2Rice Research Centre,

MARDI Seberang Perai, Seberang Perai, Penang, Malaysia; 3Centre for Artificial Intelligence & Robotics (CAIRO),

UTM, Jalan Semarak, Kuala Lumpur, Malaysia; 4University Malaysia Perlis (UniMAP), Malaysia.

Abstract MARDI has successfully developed variable rate technology (VRT) for fertilizer

application in rice precision farming. The developed system consists of field data collection, processing and analysing unit, treatment map and the variable rate applicator (VRA). Two types of field data collection have been established i.e. ground truth data sampling in grid form of selected location and unmanned aerial vehicle, UAV-based image capturing for large area. Fertilizer calculation software which is based on GAI (Green Area Index) model has been developed to calculate the fertilizer NPK (Nitrogen (N), Phosphorus (P), and Potassium (K)), urea and MOP (Muriate of Potash). Total amount of fertilizer for a specific management zone was determined. The treatment map, which indicates the amount of fertilizer to be applied based on this management zone was generated. The VRA, a tractor driven implement has been used for variable rate fertilizer application. The field computer read from treatment map, the fertilizer quantity and field location to be applied and sent the signal to the VRA controller. The field location was guided through the tractor mounted GPS. Once the spreader reached the position signal, the VRA controller triggered the metering device to control the spreader orifice at specific size equivalent to the required amount.

Keywords: heavy metal concentrations, water quality monitoring, abandoned mine, active

mine, acid mine drainage INTRODUCTION

Variable rate of fertilizer application is an approach in fertilizer management which consider the amount of applied fertilizer based on the level of the crop and field variability. The variability is corrected through fertilizer manipulation by giving correct amount of fertilizer enough to set the existing level of variability towards achieving the recommended target (reference crop) (Abu Hassan, 2001). It is expected that through this practice of fertilizer management, it will reduce the amount of fertilizer used, increase or sustain yield, improves crop quality and hence improves the environment quality. To adopt this new approach, it requires precision farming technology in fertilizer application. This practice requires the understanding of paddy crop growth variability within and between fields as well as between seasons of the same area. The crop growth variability is caused by many factors such as soil fertility, soil type, field levelness, puddling degree, water management, pests and diseases (Abu Hassan, 2004; Chan, 2013). These factors can affect the crop growth in several ways such as in the crop establishment of plant population, crop green area and colour which leads to variability within and between fields.

a E-mail: saifudin@mardi.gov.my

International Conference on Agricultural and Food Engineering (Cafei2016)

23-25 August 2016

e-ISBN 978-967-960-401-6 278

The field levelness and the degree of puddling will affect the initial crop establishment when seeding is carried out under saturated field condition. At a higher degree of puddling, some of the seed will drown while at low puddling the degree soil to seed contact is minimum. Drown seed will not established into seedling while minimum seed soil contact will hamper root encouragement to the soil. Field levelness also effect crop establishment in tillering stage. As the crop growth, the tillering process is hampering at low spot due to limited amount of sunlight and heat penetrating through the water. Soil fertility will affect the size and colour of the shoot. At low soil fertility the shoot size is small and yellowish and verse versa. The pest and disease will affect the loss of shoot size and number. The loss of shoot size will reduce the green area which will affect photosynthesis activity while the loss of shoot number will reduce the panicle bearing tiller and hence the yield. Therefore, these factors need proper crop management to achieve a good crop set. The above arguments are highlighted to show the importance of managing crop canopy. The variability of the canopy growth can be indicated by the size and colour. Precision farming technology is developed to manage this variability and one of the method is by manipulating the rate of fertilizer application. The objective of this paper is to highlight some of the finding in variable rate fertilizer application in paddy field. MATERIALS AND METHODS

The verification of the technology for variable rate nitrogen, N application was conducted at MARDI Seberang Perai beginning from first season 2014. Two sizes of uniform management zone were chosen i.e. variability within and between fields. Three plots of size one hectare each were used for variable N within field and three plots were used for variable N between fields. The plots were levelled to an acceptable Leveling Index (LI) of ±85. At this LI the plot is consider to be levelled. The levelled plot is necessary to reduce the effect of levelness on the performance of the crop. The crop management practices were followed according to rice manual standard except for fertilizer application. The Green Area Index (GAI) model was used to calculate the deviation of N content of grown crop N with the reference crop N content. The total N requirement for reference crop (120 kg N ha-1) is split to four stages of fertilizer applications. The splits of N were given at appropriate growth stages of 15, 30, 50 and 70 days at the ratio of 20:35:25:20 %. The fertilizer ratio N:P:K given were 5:3:10 at vegetative phase and 5:3:5 at reproductive phase. Before every split of fertilizer applications, the crop parameters were taken for canopy colour or chlorophyll content measured by SPAD meter and canopy size (plant/shoot population and shoot sizes) by manual sampling (twelve points per plot using 25 cm x 25 cm quadrant) and also images taken from UAV. These images will be analysed and correlated to produce a treatment map. The fertilizer is the applied by using variable fertilizer applicator based on the developed treatment map. RESULTS AND DISCUSSION

The variable rate fertilizer application technology has been successful and can be adopted in precision farming technology package. The technology consists of GAI model of reference crop for producing of treatment map, grown crop data acquisition technology and VR application. Each component technology is describes as follows: The GAI model of reference crop

The model consists of two generic equations (Figure 1) i.e Nup = SNRrGAIr (Nup = Nitrogen uptake, SNRr = Shoot N requirement taken up from soil and applied fertilizer, GAIr = green area index) which representing the temporal crop growth and Nup = SNRfGAIf + SNRsGAIs is representing the spatial crop growth performance. SNRrGAIr indicate the amount of N is taken up from the soil at a specific time and vary with time (crop growth stages). SNRfGAIf is amount of Nup from the applied fertilizer and varies depend on the GAI size (shoot size and population number). The GAI model is established by combining these

International Conference on Agricultural and Food Engineering (Cafei2016)

23-25 August 2016

e-ISBN 978-967-960-401-6 279

Nup = 18.21GAI

R2 = 0.9603

0

40

80

120

160

200

0 2 4 6 8 10Canopy size (GAI)

Can

opy

N u

ptak

e (k

g N

ha-1

)

two generic equations and is found suitable to be used in fertilizer calculation for variable N fertilizer. The model utilized two crop parameters i.e GAI and N%.

Figure 1. GAI model of reference crop.

The temporal and spatial equations indicate the relationship between Nitrogen fertilizer uptakes with the development of green area index (Abu Hassan, 2004). The temporal relationship between Nup and GAI development is linear and the paddy crop required 18.2 kg N/ha to develop one unit of green area as shown in Figure 2. This relationship is able to estimate crop N requirement as well as total N and also incremental fertilizer amount is found significant. The model is sensitive to the change of SNR and GAI. The change of SNR is due to the changes of N% in the crop canopy while the change of GAI is due to the change in shoot size and population. The spatial relationship is important for estimating the amount of fertilizer N to be spread spatially. This relationship leads to the development of decision coefficient for estimating the more or less fertilizer to be applied and depending on the protocol selected.

Figure 2. Temporal and spatial relationship of N uptake with GAI development. Protocol for variable fertilizer N and NPK application

The developed protocol is successfully to evaluate GAI model and is found useful to be adopted as one of the methods in varying fertilizer rate. It is found that the strategies of fertilizer application developed from the philosophy of "PUT MORE or LESS IN GOOD OR POOR AREA at A RIGHT TIME" allow to estimate the amount of N due to the variable of N% and GAI. A simple approach for estimating the variable N is by normalizing the grown crop data with reference crop data. This normalizing technique leads to the establishment of decision coefficient.

International Conference on Agricultural and Food Engineering (Cafei2016)

23-25 August 2016

e-ISBN 978-967-960-401-6 280

By incorporating the decision coefficient into the temporal equation and simplified, the general formula for estimating N is formulated by;

(1)

or

(

)(

) (

)

(2)

Estimation of crop grown N% and GAI and development of treatment map

The SPAD meter reading is found accurate in estimating the N% in the canopy (Teoh et al., 2010; 2012). This instrument can be adopted as a direct tool for measuring N% and as an indicator for vegetation indices for image analysis. The GAI value is obtained by manually measuring the population and shoot size. These parameters will be correlated with the images taken by the UAV (Figure 3). The captured images will be converted to SPAD reading map and population map (Figure 4) and will be further process based on GAI model using MARDI fertilizer calculation software to calculate the total amount of fertilizer, fertilizer application rate and to generate a treatment map.

Figure 3. Image capturing, processing, fertilizer calculation and development of treatment

map.

(a) (b) Figure 4. Image captured by UAV system to generate; (a) SPAD reading map (b) Population

map.

International Conference on Agricultural and Food Engineering (Cafei2016)

23-25 August 2016

e-ISBN 978-967-960-401-6 281

Variable rate applicator The knapsack blower could be used for variable fertilizer application based on the

develop treatment map as a low cost technology. It needs a proper boundary marking during applying variable rate even-though with the helped of backpack GPS. However, with the availability of variable rate applicator mounted to a light weight high clearance prime mover made the work much easier as shown in Figure 5. The necessary basic requirement of the field condition as being reported by Abu in (Abu Hassan, 2007). The variable rate applicator has been tested, evaluated and has shown an encouraging results.

Figure 5. VRT Application for Fertilizer. CONCLUSIONS AND FUTURE WORK

The following conclusions can be drawn from the study: – The developed package of precision farming technology capable of applying variable

rate fertilizer according to the measured grown crop parameter by comparing with reference crop parameters.

– The system can be further simplified if the research plot is used for one management zone. Future work is to apply this technology for large scale paddy production through upscalling project with MADA.

ACKNOWLEDGEMENTS

The authors would like to acknowledge researchers referenced throughout the paper and also to whom that had fruitful discussions and collaboration with the authors. Literature cited

Abu Hassan, D. (2001). Monitoring crop canopy development for nutrient management. Proceeding National Conference on Agriculture and Food Mechanization. 30-31 Oct, K. Lumpur

Abu Hassan, D. (2004). Canopy management for variable N application. Proceeding APAC Conference on Precision Farming, 11-13 May, PWTC. K. Lumpur.

Abu Hassan, D. (2007). Light weight high clearance prime mover for precision inputs applications. Proceeding World Engineering Congress, Dec.2007, Penang.

Chan, C.W. (2013). Precison Farming: The Way Forward in Mechanized Agriculture. Research Inaugural Lecture, 28 February 2013 MARDI, Serdang. ISBN 978-967-936-613-6.

Teoh, C.C, Chan, C.W. and Abu Hassan. D. (2012). Paddy field plot boundary extraction and fertilizer treatment maps using remote sensing technique. Workshop for SMEs on "Appropriate precision farming for enchancing the sustainability of rice production". 15 Oct 2012, Kuala Lumpur.

Teoh, C.C, Mohd Syaifudin, A.R., Muhammad Isa, O. and Badril Hisham, A.B. (2010). Estimation of tiller numbers for rice plant population monitoring using image processing and analysis techniques, 473-477. Proc. of National Rice Conference, 28-30 June 2010, Perak.