Yield mapping of cotton crop in Greece

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Keywords: yield mapping, cotton, precision farming, GPS ... On one hand the economical competition of global markets due to the high cost of production.
Yield mapping of cotton crop in Greece 1

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Ath. Markinos , T.A.Gemtos , L. Toulios , D. Pateras , G. Zerva 1

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Laboratory of Farm Mechanization, Faculty of Agriculture, University of Thessaly, Fytoko Street,

N. Ionia, Magnissias 38446, Tel: +30421093228, Fax: +30421093144, e-mail: [email protected] 2

NAGREF/ISM, Theofrastou 1 41335, Larissa, e-mail: [email protected]

Abstract. Yield mapping is considered as the basic element of every precision agriculture system. They were developed the last decade in crops like grain. Lately, commercial mapping systems for cotton were developed. Their application can maximize the economic and minimize the environmental adverse effects for Greece. During the harvesting period of 2001, a cotton yield mapping system was evaluated in fields of Karditsa area. The results of the operation in two fields, with area 4.3 and 1.7 ha respectively, are presented in the present paper. This study showed that every field studied had distinctive zones of equal yield. There was also a significant difference in yield between them despite the small field size. Thus, the variable rate application of inputs may provide economic advantages even in small fields, as is the situation in Greece. Keywords: yield mapping, cotton, precision farming, GPS

Introduction Agriculture, one of the conventional sectors of the economy, at the 21st century, faces new challenges: On one hand the economical competition of global markets due to the high cost of production and on the other hand the environmental pollution and the shortage of physical sources cause problems. Science for one more time is called to find solutions to solve these problems. In the current phase there are research efforts in two main directions: biotechnology and new technology. The progress of biotechnology drives to the appearance of new, more productive varieties with better resistance to enemies and changes in climatic conditions, reducing the use of chemical and physical inputs. On the other hand, the advances in electronics and computers generate new techniques to maximize the farmer’s profit and to protect the environment. In the later framework, a new technique known as precision agriculture, or precision farming or site specific management, tries to give solutions (Mass, 1998). Conventional production management, considering the field as a uniform area with constant physical, chemical and morphological properties, increases the cost of production and boosts the environmental encumberment. That is to say, the farmer applies the inputs, like fertilizing, with constant rate, which means a waste in some parts and a deficiency in others. That refers to all of the agricultural inputs and applications like, seeding, irrigation, spraying, etc. Therefore, conventional farming is unable to achieve the maximum production with the minimum inputs and minor environmental impacts.

Precision agriculture management considers field as a large number of small fields. In these elementary fields, soil properties are constants (Valco et al., 1998). So in each of them, could be followed a different farm management practice. That assumes the existence of processing and storing systems for a great amount of information. The progress in computers, in geographical positional systems, in sensor and variable rate technology, contributes in easy and rapid storage and processing of data (Jallas et al., 1999). Precision farming system A precision agriculture system can be represented as in Figure 1. According this diagram, the whole procedure is divided in three different phases. The first one relates to the acquisition of crop data, mainly old yield maps and soil analysis data. During the crop development, the system gathers more data like weather data, crop observation data and radiometry data (Elms and Green, 1997). The use of appropriated hardware automates most of the procedures of this phase. Scout Data

Soil Analysis

Weather Data

Crop Data Acquisition

Yield Maps

Data Processing Crop Model

Implementation in the field (VRA)

Radiometry Data Library

Figure 1: A complete system for precision agriculture Second phase concerns the processing and calculations on the data that have been gathered. The way of combining data in every system depends on the crop type and the implemented algorithm (ΜcCauley, 1999, McKinion et al., 2001). The existence of an advanced upgradable library or database provides the system with the necessary intelligence to work out many different conditions and solutions. Third phase relates to the implementation and adaptation of cultivation practices according to system results. It needs the modification of the equipment to accommodate variable rate application of agricultural inputs in the field area (Bowers et al., 2001). This specific operation targets to counterbalance the variations of the physical and chemical soil properties from point to point in the field. Yield mapping system in cotton The basic procedure of data acquisition system is the yield mapping during the crop harvest. In crops like grains, the operation of these systems has already a life more than a decade. All this time, many commercial systems has been developed from different manufacturers (Reyns

et al., 2002). The precision of those systems has reached a satisfactory level due to improving on geographical positioning systems and continuous research for more accurate sensors and debugging algorithms (Blackmore and Moore, 1999). Current systems have sensors based on different working operations like mass flow, volumetric flow, impact and others (Arslan and Colvin, 2002). Sensors recording the cotton flow have recently developed. The development was late due to the difficulty to measure the erratic shape and texture of cotton during the conveyance with air stream to the storage basket (Searcy et al., 1997b). The majority of cotton sensors in the market, at the moment, are based on measuring the volume of cotton flow through air ducts from the picking point to the basket of the machine. These sensors fitted on the air ducts consist of a transmitter of infrared light beam and an opposite receiver measures the incident light (Wallace, p.3, 1999). Using a calibration factor the system converts the volumetric flow to mass flow of cotton every moment. A GPS receiver with the corresponding antenna provides the system with the actual position of the machine in the field area. Additional sensors inform the system about the vertical position of picking units and count the actual speed of cotton picker. All these parts of equipment connected to the main control unit, installed in the cabin of the machine. This paper presents the evaluation of a yield mapping system application in fields of central Greece. Materials and Methods During the 2001 harvesting period, a yield mapping system was used to provide maps for cotton fields in central Greece. At October 13th of 2001 a first hand cotton picking was carried out in two fields with area 4.3 and 1.7 ha respectively, located at Myrina, in Karditsa prefecture. A two-row picker was used. The two fields had been planted with Carmen variety in row spacing of 0.96 m. The yield mapping system had chosen based on functionality and upgrade capability and comes from Australian Farmscan. The whole kit consists of the main control unit; the two pairs of infrared yield sensors, the speed and units position sensors and the GPS antenna. The central unit is the basis of the whole system. During the operation, it gathers and stores the sensor data and simultaneously shows them on the incorporated LCD display. Central unit was installed in the driver’s cabin (Figure 2) in order to give the ability to the driver of a clear view of the yield, average yield, harvested area and the actual weight of cotton in the basket. Driver has also the ability to control the operation of the whole system and if there is any problem, system visually informed about the point of trouble (Figure 3). The main unit stored all the data at constant time periods adjusted by the user. In the current application, time interval adjusted at 2s. Logged data are a combination of geographical coordinates (latitude and longitude) of the spot of cotton picker, with the speed and yield at the same point. All data stored in a memory card (SRAM) with 2-MB capacity (Figure 4). This size is sufficient for 24 hours of continuous system operation.

Figure 2: The central unit installed in the cabin of the cotton picker

Figure 3: The screen of the central unit

Figure 4: The yield data stored in this 2-ΜΒ memory card SRAM

Yield sensors constitute another basic component of the mapping system. As has already reported, every sensor is a pair of transmitter and receiver fitted on the chutes of cotton picker. An infrared light beam generated between transmitter and receiver, interrupted from the cotton flow to the basket. That is like taking photos of conveying cotton many times per second (Wallace, 2000). Two sensors were used for 2-row pickers according to the manufacturer. Two pairs of sensors were installed on the two chutes of picker (Figure 5). The installation point was chosen at the middle of the air ducts length, because there is the minimum possibility of cotton overlapping while running through light beam (Farmscan installation guide, 2000). For the proper installation of yield sensors required to made holes at the chutes on both sides, in order to face exactly each other. As mentioned, these sensors measure the volumetric flow of cotton and a suitable calibration factor was needed to make the conversion in mass flow. The value of this factor depends on the cotton variety, the harvest time (moisture) and the degree of defoliation. Therefore, it needs a new adjustment at every different field. The calibration factor was found by importing the value of actual weight of the first full basket. The System gives also the ability to adjust the factor after weighing the whole picking mass of the field and importing it to the software. The GPS antenna fitted on the roof of machine cabin, at least 1 meter away from other antennas due to interference phenomenon (Figure 7). It was connected to the GPS receiver that is built-in the main control unit.

Figure 5: One pair of the infrared sensors installed on the chute (transmitter and receiver)

Figure 6: The receiver

Figure 7: The GPS antenna on the roof of the cabin

Two sensors complete the whole system installation. The first is the speed sensor adapted on the axle of drive wheel. Consists of a magnet and a magnetic switch. The system computes the speed of the machine compensating the signal of this sensor with the transposition of the GPS signal for better accuracy. The second sensor informs the control unit for the position of the picking units (Figure 8). When the unit was risen the system canceled storing the yield data. That occurred when the machine was turning at the edge of the field, or in any other stoppage.

Figure 8: On/Off switch on the arm of the picking units Stored data in SRAM card was transferred in a personal computer with card reader connected, or in a laptop through the PCMCIA port. The accompanying software Farmscan Data Manager used for the processing and displaying of yield data. It is a simple GIS based package for graphical spatial representation of yield in field area. The system operation was successful and there weren’t any problems. One point has to be mentioned, is the attention in the sensors cleaning from dust. When the system operated without sensor cleaning for long period of time, there were eye blockages of the sensors and the system stopped measuring. For smooth operation of the system, the yield sensors should be cleaned at every picking of a full basket.

Results Cotton yield during the first machine passage, after weighing it, was 13.2 t for the 4.5 ha field and 5.03 t for the 1.7 ha field. That gave a yield of 3070 kg/ha and 2960 kg/ha for the two

fields respectively. Using the yield data collected during the same picking and the software of the system, the maps shown in Figures 9 and 10 were prepared.

Figure 9: The Yield Map for the 4.3 ha field The two maps show the yield variability in the fields. It is quite clear that yield zones can easily be drawn giving yields from 1000 kg/ha up to 4500 Kg/ha.

Figure 10: The Yield Map for the 1.7 ha field

Discussion The two maps show that even in small fields there are distinctive yield zones. Usually, the farmer applies the inputs according to the average yield. The maps show that this practice is not appropriate as in parts of the field the inputs are in surplus and in some less than the optimum. It is obvious that the first step after yield mapping is to find the reasons of this yield variability. A series of measurements should study soil properties (texture, chemical and physical properties), weed populations, plant populations and plant condition during the growing season, pests attacks etc. A grid should be drawn in the field that would define the sampling points in the field. Data should be used to draw thematic maps showing the

variability of each property in the field. Additional data will be collected using equipment able to map field soil conductivity up to a depth of 0.90 m (Drummond et al., 2000). These maps will be over-imposed and in connection to the yield map would indicate the causes of yield variability. Knowing the cause, is the basis for planning variable rate applications of the inputs in the next season crop. Appropriate software should be used to program variable rate settings for the application machinery, which should have the ability to change the settings when necessary. The same field will be planted with cotton in the next year’s growing season. Data will be collected during the growing period. Additional measurements of the field reflectance will be taken in an attempt to correlate yield with NDVI (Elms and Green, 1997). All collected data will form a data base which will be used to support farmer decision in order to achieve optimum resources use, either by maximizing yields, or by reducing inputs. Concluding, it is clear that yield mapping can offer the basis for variable rate applications in cotton crop. Cotton is a difficult crop requiring precise handling during the growing period. It appears that the presented data can assist farmers in their crop management. A question remains to be answered whether the investment cost for precision farming soft and hard ware is justified by the economic returns (Auernhammer, 2001).

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Study in Mississippi Cotton Fields. Proceedings of Beltwide Cotton Production and Research Conferences, Orlando, FL, 3-7 January, 1999. Mass, S.J., 1998. Remote sensing for agriculture in the next decade. In: Dugger, P., Richter, D.A. (Eds.), Proceedings Beltwide Cotton Conference, 5-9 January 1998, San Diego, CA. National Cotton Council of America, Memphis, TN, pp. 36-38. McCauley, J.D., 1999. Simulation of Cotton Production for Precision Farming. Precision Agriculture, 1, pp. 81-94. McKinion, J.M., Jenkins, J.N., Akins, D., Turner, S.B., Willers, J.L., Jallas, E., Whisler, F.D., 2001. Analysis of a precision agriculture approach to cotton production. Computers and Electronics in Agriculture 32, pp. 213-228. Reyns, P., Missotten, B., Ramon, H., De Baerdemaeker, J., 2002. A Review of Combine Sensors for Precision Farming. Precision Agriculture, 3, pp. 169-182. Searcy, S.W., Motz, D.S., Inayatullah, A., Goering, K.J., 1997b. Cotton yield mapping. In: Dugger, P., Richter, D.A. (Eds.), Proceedings Beltwide Conference, 6-10 January 1997, New Orleans, LA. National Cotton Council of America, Memphis, TN, pp. 601-603. Valco, T.D., Nichols, R.L., Lalor, W.F., 1998. Adopting precision farming technology for cotton nutrition. In: Dugger, P., Richter, D.A. (Eds.), Proceedings Beltwide Conference, 5-9 January 1998, San Diego, CA. National Cotton Council of America, Memphis, TN, pp. 627629. Wallace, T.P., 1999. Small plot evaluation of an electro-optical cotton yield monitor. Computers and Electronics in Agriculture, 23, pp. 1-8. Wilkerson, J.B., Kirby, J.S., Hart, W.E., Womac, A.R., 1994. Real-time cotton flow sensor. ASAE Paper No. 941054. American Society of Agricultural Engineers, St. Joseph, MI.