Monitoring of Crops through Satellite Technology

UTF/PAK/101/PAK Final Report

Monitoring of Crops through Satellite Technology UTF/PAK/101/PAK

Report prepared by

Dr. Attila Bussay Yield Forecasting Expert-CTA FAO & Ibrar-ul-Hassan Akhtar (SUPARCO)

FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS Rome, June- 2009

Table of Contents 1.

INTRODUCTION ......................................................................................................................................... 4 1.1

2

THE OBJECTIVES OF UTF/PAK/101/PAK FAO PROJECT........................................................................ 4

THE WHEAT CROP ................................................................................................................................... 5 2.1 AVAILABLE DATA.................................................................................................................................. 5 2.2 WHEAT YIELD/PRODUCTION FORECAST & ESTIMATION ....................................................................... 5 2.2.1 Statistical Models based on Fertilizer Inputs (elaborated by Mr. Riad Balaghi) ........................... 6 2.2.2 Statistical Models based on Crop Cuttings Inputs (elaborated by Mr. René Gommes) .................. 6 2.2.3 Statistical Model based on Maximum NDVI for Production Estimation (elaborated by Mr. Attila Bussay and Mr. Ibrar-ul-Hassan Akhtar) ...................................................................................................... 7 2.3 COMPARISON OF FORECASTED AND OFFICIAL WHEAT YIELD OF 2007/08 ............................................. 9

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INPUT DATA FOR KHARIF CROPS MODEL DEVELOPMENT..................................................... 10 3.1 CROPPING PATTERNS AND CROP CALENDAR OF PAKISTAN ................................................................. 10 3.2 NDVI SATELLITE IMAGES .................................................................................................................... 12 3.2.1 Preparation of NDVI satellite images ............................................................................................. 12 3.2.2 Smoothing ....................................................................................................................................... 13 3.2.3 Determination of main phenological stages ................................................................................... 13 3.2.4 Calculation ..................................................................................................................................... 14 3.2.5 Comparison..................................................................................................................................... 15 3.3 METEOROLOGICAL DATA FOR KHARIF SEASON................................................................................... 15 3.3.1 Calculation of Potential Evapotranspiration .................................................................................. 17 3.4 FERTILIZER DATA ................................................................................................................................ 17 3.5 YIELD DATA OF KHARIF CROPS ........................................................................................................... 19 3.5.1 Statistical Yield Data for Kharif Season ......................................................................................... 19 3.5.2 Crop Cuttings Data ......................................................................................................................... 19

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IDENTIFICATION OF CROPS ON SPOT VGT & DEVELOPMENT OF CROP MASKS ........... 20 4.1 S-VALUE CONCEPT ............................................................................................................................... 21 4.2 FREQUENCY DISTRIBUTION .................................................................................................................. 24 4.3 SPATIAL FREQUENCY DISTRIBUTION ANALYSIS (SFDA) .................................................................... 25 4.4 CROP RELATED S-VALUE ..................................................................................................................... 27 4.5 DEVELOPMENT OF KHARIF CROP MASK .............................................................................................. 29 4.5.1 Vector Grid Generation .................................................................................................................. 29 4.5.2 Zone development and Extraction of Statistics ............................................................................... 29 4.5.3 Final Crop Mask ............................................................................................................................. 30 4.6 STRENGTHS AND WEAKNESS ............................................................................................................... 31

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CROP-WISE ZONING AND MODEL CALIBRATIONS...................................................................... 33 5.1 COTTON ............................................................................................................................................ 33 5.1.1 Introduction .................................................................................................................................... 33 5.1.2 Zone-wise selected districts and their yield trend analyses ............................................................ 33 5.1.3 Cotton models ................................................................................................................................. 35 5.1.4 Zone-wise Models ........................................................................................................................... 36 5.2 SUGARCANE ......................................................................................................................................... 37 5.2.1 Introduction .................................................................................................................................... 37 5.2.2 Zone-wise selected districts and their yield trend analyses ............................................................ 37 5.2.3 Zone-wise Models ........................................................................................................................... 39 5.3 RICE ................................................................................................................................................... 40

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5.3.1 Introduction .................................................................................................................................... 40 5.3.2 Types and Forms of Rice ................................................................................................................ 41 5.3.3 Zone-wise selected districts and their yield trend analyses ............................................................ 41 5.3.4 Zone-wise Models ........................................................................................................................... 43 5.4 STRENGTHS AND WEAKNESS ............................................................................................................... 44 6

SUMMARY ................................................................................................................................................. 45

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LITERATURE ............................................................................................................................................ 46

APPENDICES .................................................................................................................................................... 47 APPENDIX 1....................................................................................................................................................... 49 APPENDIX 2....................................................................................................................................................... 50 APPENDIX 3....................................................................................................................................................... 51 APPENDIX 4....................................................................................................................................................... 52 APPENDIX 5....................................................................................................................................................... 53

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1.

INTRODUCTION

1.1

The objectives of UTF/PAK/101/PAK FAO Project

The overall prime objective of the UTF/PAK/101/PAK FAO project was to provide reliable agricultural information with timely provision of agricultural statistics to the government of Pakistan. The direct goal of this FAO project was to support the Pakistan Space and Upper Atmosphere Research Commission (SUPARCO) for execution of PC-I project on “Monitoring of Crops through Satellite Technology”. Secondly, this project was to provide expertise in the development of agro-meteorological crop yield forecasting system, the remote-sensing based acreage estimations, and finally crop production forecast/estimation. The main goal of this FAO project was to provide technical support to the Pakistani institutions/organizations on the basis of proposed actions of previous FAO mission (Mr. René Gommes and Mr. John Latham; 8 to 14 May, 2005; Islamabad, Pakistan) regarding this issue. To the successful accomplishment of this project, it was necessary to strengthen the co-operation between the stake-holders. This project had to rely on local/provincial organizations to achieve the maximum extent of the capacities. To achieve the success, it was also necessary to involve the federal and provincial specialists concerned to the yield-forecasting in the planning, data provision, developing, testing, evaluation and practical application of the procedures. The results of the project will be improved crop-yield modeling and crop-area estimation techniques and will serve to forecast productivity as a basis for determination of food deficit/surplus situations. This information is directly useful in pricing policies for commodities and derivative commodities. The crop-yield forecasting system combined with well-based agricultural statistics could serve as an advanced regional or national-wide early warning. To keep the sustainability of crop-yield forecasting system, there was the need of continuous co-operation of stake-holders for systematic collection and dissemination of following types of data between them; crop acreage information, climatological and actual weather timeseries, low-resolution satellite-based vegetation images (NDVI indices), farm inputs, fertilizations data, irrigation water data, crop cuttings and agricultural yield statistics. The further aim of this project was to train agrometeorological yield forecasting and related techniques required for Pakistani experts.

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2

THE WHEAT CROP

Detailed report on wheat yield forecast was submitted in 2008 June to the FAOPK office and to the SUPARCO. Here we would like to summarize shortly the elaborated forecasting methods for Pakistan. The first wheat yield forecast was released March 20, 2008 followed by final estimates on April 20, 2008. Finally evaluation of the different forecasting methods was carried out through comparing the forecasted yield with the official wheat statistics published by the Federal Bureau of Statistics for 2007/2008 Rabi season in October 2008. Wheat makes the largest component of Pakistan’s grain basket and is a global relished food commodity of Pakistan. This crop occupies approximately 40 percent of cultivated area of the country, which is the largest cropped area under any single crop. In Rabi season (wintertime) wheat is the dominating crop of Pakistan. Wheat is a major food crop cultivated mainly in the Provinces of Punjab and Sindh (Indus River Valley). In upper and lower Sindh and in Punjab 90 percent of wheat is irrigated. These two provinces produce more than 90 percent of the wheat of the country. In NWFP, 40 percent wheat comes from irrigated areas and 60 percent from rain-fed areas. In Balochistan, major wheat growing areas are rain-fed, and irrigated wheat is mainly confined to pat feeder zone (Nasirabad and Jafarabad districts mainly). The sowing of crop, crop growth at various phenological stages and size of production are highly crucial for the food security. Our aim was to develop a wheat yield forecasting method for Pakistan involving the new technical possibilities to estimate the yield (tons/ha) or production (tons) some months or weeks before harvest.

2.1

Available Data

The following data-sets were available to elaborate wheat yield forecasting methods and procedures for Pakistan:  District-wise wheat yield and acreage statistics for Punjab from 1986/87 season and for Sindh, Balochistan and NWFP from 1995/96 until 2008  Wheat crop cuttings data (standard sampling technique in Pakistan) of 19 districts in Punjab, Sindh and NWFP for 1993-2007 time-period originated from t he Provincial Crop Reporting Services (PCRS)  Satellite NDVI (Normalized Difference Vegetation Index) images (1992-2008)  Province and district-wise monthly fertilizer off-take data, which mainly include the nformation received from private fertilizer producing and importing companies. These includes the consumptions of nitrogenous, phosphates, potash and other micro-nutrients (Zinc, Boron etc). This data is compiled into a database by Federal Government organization i.e. National Fertilizer Development Centre (NFDC). Nitrogen, phosphorous, potash and total nutrient off-take (commercial retail) was calculated from NFDC fertilizer database from 1992-2008.

2.2

Wheat Yield/Production Forecast & Estimation

SUPARCO was able to report on area sown under wheat crop based on satellite images classification and Area Frame sampling techniques, make a production forecast in March 2008, followed by release of production estimate in April 2008. The first wheat yield

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estimates were finalized on March 20, 2008. Here we would like present the previously elaborated three independent statistical models. 2.2.1

Statistical Models based on Fertilizer Inputs (elaborated by Mr. Riad Balaghi) Statistical models were developed at Province and National Level based on the total nutrients applied during the year for crop production and its relationship with yield of a wheat crop. Linear regression shows high correlation between published crop statistics and fertilizer consumption (off-take) at provincial and national levels. Statistical analysis reveals that fertilizer alone explains almost 84 % of variation (r2 = 0.837) in yield for wheat crop based on time-series from 1986/87 till 2006/07 for Punjab province and 79 % for Sindh province. The yields forecast by this model are published in the Table 2.2. The forecasts of this model can be improved by using SPOT-VGT NDVI images. Including province-wise weighted average NDVI for last dekad of February (Punjab) and 1st dekad of January for Sindh beside nutrient supply data gave better estimates for wheat yield. The determination coefficients are 0.713 and 0.778 for Punjab and Sindh for 1993-2006 timeperiod, respectively. These results are shown in the Table 2.2. To provide reliable forecast using these method it is crucial to have good estimates of actual fertilizer consumption or off-take. 2.2.2

Statistical Models based on Crop Cuttings Inputs (elaborated by Mr. René Gommes) There is the possibility to develop a wheat crop yield forecasting method using crop-cuttings data together with crop statistics and NDVI images. Linear regression was calculated between geographically identified crop cuttings and geo-located SPOT-VEGETATION NDVI values at farm level. This method is based on the assumption that crop cuttings are more representative of real yields, than official crop statistics. The usage of crop cuttings is very advantageous because these data are less biased and mirror the real agricultural situation. The crop cut data are in more direct relationship with the actual localized NDVI values than the district yield averages. The scientific statistical analysis shows strong multiple linear relationship between the crop cuttings yield and 3 independent variables as NDVI patches, district-wise statistical yields and years. This relationship could be characterized with r=0.8154 correlation coefficient. The regression equation can be expressed by the following form:

YCCest  3.0069  NDVI Feb  2.6503  Yavg  0.1424  Year  285.6059 , where (1) - YCCest is the Crop Cuttings estimated yield [kg/plot] - NDVIFeb is the localized average NDVI values for month of February [0-1, unitless], - Yavg is the district-wise statistical average Yield for 1993-2006 time-period [tons/ha], - Year is the calendar year. The NDVI average of February was used in the process of yield estimation because the preliminary calculation has shown good relationship between yield and this value. Similar equation could be used to deliver somewhat more accurate yield forecast in April using the NDVI averages of March or first dekad of April. Equation (1) was used to estimate the yield of every district. We made an assumption that one calculated crop cuttings value can represent the whole crop-stand of every related

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district. To take this calculation we used the average NDVI for district to determine the YCCest value. Further statistical equation was used to estimate YDISTest district-wise yield on the basis of calculated crop cuttings. The mathematical form of this equation is the following:

YDISTest  0.2265  YCCest  0.5963.

(2)

Practically this Equation (2) was used to calibrate the scale of crop cuttings into the scale of yield. The first estimates of this model for 2008 are published in the Table 2.2. 2.2.3

Statistical Model based on Maximum NDVI for Production Estimation (elaborated by Mr. Attila Bussay and Mr. Ibrar-ul-Hassan Akhtar) Wheat production estimation method was based on pixel-wise maximum NDVI value for wheat season. An assumption was made that the NDVI values are related mainly to the wheat crop because this is the main crop of Rabi season. This estimation technique was developed on province level data for Punjab, NWFP and Balochistan. Maximum NDVI images were used as an indicator of wheat maximum development during each season for a given pixel. The benefit of maximum NDVI values is the reduction of the possible errors of original satellite images during the season. This maximum NDVI pixel values were used for weighted average calculation at province level to identify average maximum development of a crop at province level. Statistical analysis was carried out to determinate the strength of relationship between production/yield with NDVI and year based trend. We compared the results of regression analysis and the correlation coefficient proved better for wheat production than yield. Finally, the wheat production was estimated using multiple linear regression equation. We applied this method with little modification to accommodate the existing differences among provinces (rain-fed and irrigated agricultural areas). For Punjab a crop mask (SAM2008) was used for extraction of pixels of arable land. In Balochistan and NWFP provinces the wheat is cultivated mainly under rain-fed conditions and the cropping patterns have high variability in space and time. This was the basis of usage of all pixels of NDVI images for these provinces at district level. Punjab production was estimated for 2007/08 Rabi season from maximum NDVI values for 1 km2 area on crop masked NDVI satellite images. It was found extremely strong (r2=0.941) correlation between the independent variables and wheat production (Table 2.3). Balochistan and NWFP provinces production estimation was taken an assumption that if the natural vegetation is in good condition then most probably the agriculture crop will be also in good condition, as most of the cultivated area come under rain-fed conditions. The results are acceptable for these provinces and the related determination coefficients of wheat production relationship are published in the Table 2.3. In Sindh province this method was not able to provide acceptable yield/production forecast due to the mixed cropping patterns. Sindh production was estimated from existing relationship between yield of Punjab and Sindh provinces (Table 2.3).

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Table 2.1: Official Wheat Statistics for 2007/08 Rabi season published by Federal Bureau of Statistics (FBS) in October 2008 S.No 1 2 3 4 5

Province Punjab Sindh NWFP Balochistan Pakistan

Table 2.2:

Area (1000 ha)

Production (1000 tons) 15607.6 3411.4 1071.8 868.6 20958.8

6402.0 989.9 747.4 410.5 8549.8

Yield (kg/ha) 2438 3446 1434 2116 2451

Wheat Yield Estimates 2007/08 (tons/ha) for Pakistan (20th March 2008) Method

Province

Regression Model (Fertilizer Inputs) Riad Balaghi technique (1)

Pakistan Error

Regression Model (Fertilizer+NDVI) Riad Balaghi technique (2)

Regression Model (Fertilizer Inputs) Riad Balaghi technique (3)

Regression Model Regression Model (Fertilizer+NDVI) Rene Gommes Riad Balaghi technique (5) technique (4)

2600

2416

2523

6.08 %

-1.43 %

2.94 %

Punjab

2733

2802

2515

2617

2516

Error

12.10 %

14.93 %

3.16 %

7.34 %

3.20 %

Sindh

2748

2814

2585

2603

2581

Error

-20.26 %

-18.34 %

-24.99 %

-24.46 %

-25.10 %

NWFP

2420

Error

68.76 %

( =high accuracy,

= medium accuracy,

= low accuracy)

(1) Based on total 2007/2008 fertilizer consumption estimate (NFDC) (2) As (1), but including average crop zone NDVI for last dekad of February (Punjab) and 1st dekad of January for Sindh (3) Fertilizer as (1), but adjusted based on actual Punjab fertilizer consumption (April 2007 to January 2008). The national value includes all the provinces (4) As (3), but including average crop zone NDVI for last dekad of February (Punjab) and 1st dekad of January for Sindh (5) Based February 2008 average crop zone NDVI after calibration against 2000-2006 crop cuttings by provincial CRSs, aggregated from District level. Provincial and national yields computed from district values using 2007 areas as the weighting factor. The national value includes mainly Punjab and Sindh, plus three districts in NWFP.

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Table 2.3: Production estimates for Wheat 2007/08 coefficient (r2) and Mean error of estimation [%] Forecasted S.No. Provinces r2 Value Production

[million

tons],

Determination

Official Production

Error

1

PUNJAB

15.89

0.941

15.61

1.81 %

2

SINDH

2.72

0.734

3.41

-20.27 %

3

NWFP

0.99

0.652

1.07

-7.63 %

4

BALOCHISTAN

0.75

0.800

0.87

-13.65 %

5

PAKISTAN (TOTAL)

20.35

N/A

20.96

-2.90 %

( =high accuracy,

2.3

= medium accuracy,

= low accuracy)

Comparison of Forecasted and Official Wheat Yield of 2007/08

The official wheat yield, acreage and production were released at the end of October 2008 by Federal Committee on Agriculture FCA (Table 2.1). Evaluating the error values given in the Table 2.2 and Table 2.3 the wheat yield and production forecast of 2007/2008 Rabi season can be consider as successful for Punjab province and Pakistan. It is important to underline that the production forecast involves the accumulated error of area (acreage) and yield forecast, too. The detailed examination of accuracy showed significant differences between provinces and methods. The best forecasts were originated from the following 3 methods:  Regression Model based on René Gommes technique  Regression Model based on Bussay & Ibrar technique  Regression Model based on Riad Balaghi technique using actual Punjab fertilizer consumption (April 2007 to January 2008). The predicted figures for Pakistan and for Punjab province were quite accurate. Generally the error remained below 10 percent. Using the most promising, highly accurate methods the absolute difference was less than 3%. The Bussay & Ibrar technique provided useful results for NWFP and Balochistan in spite of some underestimation. The relative error exceeded 7%, but the absolute error remained below 0.12 million tons both case. This prediction seems to be acceptable taking into consideration the fact that the wheat is cultivated mainly under rain-fed condition in Balochistan and NWFP provinces and the acreage changes in great extent here. The red color on the case of Sindh province indicates low accuracy for all methods. The forecasted production failed more than 20% which is equal with about 0.7 million tons wheat. It is difficult to explain the reason of this high underestimation. Probably the mixed cropping patterns play important role in this failure or some biasness originated from yield/production statistics originated at district/provincial level. It is obvious that further continuous work and detailed investigation needed to improve the yield forecasting methods. Especially for Sindh province there is a need to elaborate an independent new method for wheat yield forecast incorporating the crop identification techniques, too.

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3

INPUT DATA FOR KHARIF CROPS MODEL DEVELOPMENT

3.1

Cropping Patterns and Crop Calendar of Pakistan

There are two main crop growing periods in Pakistan. These are called Kharif (summer half year) and Rabi (winter half year) seasons. By tradition, the crops are counted according to their seasons of harvest. All crops harvested around spring or follow up are called Rabi as the word literally means spring. The crops harvested in autumn, on the same analogy, are called Kharif crops. Similarly, the crops are counted in the financial year of harvest. A crop of sugarcane harvested in October 2006 onwards will be called sugarcane 2006/07, although it was sown in February 2006 which is a previous financial year (2005/06). Rabi crops include e.g. wheat, mustard, gram, lentils. The Kharif crops include e.g. sugarcane, cotton, rice, maize etc. Remarkable experiences were collected elaborating the wheat yield forecasting methods for Pakistan. The main crop of Rabi season is the wheat. The agricultural territory is pretty homogeneous in the winter half year. Most of the pixels are homogeneous and the NDVI values are mainly originated from only one crop. The NDVI images bear direct related information about the status and development of the wheat crop. Only exception could be probably the warmer Sindh province where the cropping patterns are more complex and various in the Rabi season. The cropping patterns of Pakistan in Kharif season are quite difficult. The farmers tend to follow a cropping pattern of the agro-ecology of the region. For instance, in southern Punjab cotton-wheat rotation is ordinary. Similarly, in Kallar tract of Punjab rice-wheat rotation and in Peshawar valley sugarcane-sugarcane-wheat rotation is quite common. The farmers try to grow something of everything to as a doctrine of food security and also meet their daily requirements viz. cultivated/industrial crops, spices, vegetables, fodders, fruits and fuel wood. This introduces an element of complexity in reflections and followed up measurements. Inter/mixed cropping as growing of crops in orchard or mixed cropping affects the reflectance values and makes it difficult to estimate area of crops. The average field size is small in Pakistan, too. To produce crop yield forecasting for Kharif season is a hard task. The main difference between the Rabi and Kharif season is the unlike cropping patterns. In the Kharif season several arable crops are cultivated. Some districts are relative homogenous like the rice zone, but the mixed neighboring cropping fields are general over Punjab, Sindh and NWFP provinces. It is crucial to differentiate the crop-stands on the satellite images to produce reliable yield forecast. Extensive work was done to identify the different crops on the satellite image and to locate the relatively homogeneous (“pure”) pixels, where one crop cultivated in majority of area. We avoided using the disturbed information content of very mixed pixels for crop yield forecasting. The involvement of these “bad” pixels into calculations notably could decrease the accuracy of estimations.

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The crop calendar and the general phenological phases of main crops of Pakistan are presented on the Figure 2.1 and in the Table 2.1, 2.2 and 2.3.

Figure 2.1:

Crop

Sugarcane Spring Crop

Sugarcane Autumn Crop Table 2.1:

Crop

General Crop Calendar of Pakistan

Province

Sowing Time

Harvesting Time

Punjab

15th February - 20th March

November to March

Sindh

1st February - 15th March

November to March

NWFP

15th February - 20th March

November to March

Punjab

1st - 30th September

November to March

Sindh

1st September - 15th October

November to March

NWFP

1st - 30th September

November to March

Crop Calendar of SUGARCANE

Province

Sowing Time

Harvests

Punjab

May - June

Sept. - Dec.

Sindh

March - May

July - November

NWFP

May

August - November

Balochistan

May

August - November

Cotton

Table 2.2:

Crop Calendar of COTTON

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Crop

Rice

Province

Nursery Sowing

Field Transplanting

Harvesting

Punjab

20th May - 20th June

20th June- 31st July

Sept. - November

th

th

th

Upper Sindh

8 May - 15 July

8 June -15 August

Sept. - November

Lower Sindh

16th April - 7th July

16th May - 8th August

Sept. - November

st

NWFP

Table 2.3:

st

1 May - 31 May

Balochistan

3.2

th

st

th

21 May - 30 June

Sept. - October

20th May - 30th June 20th June - 30th July

Sept. - October

Crop Calendar of RICE

NDVI Satellite Images

3.2.1 Preparation of NDVI Satellite Images Maximum value composite dekadal (10 days) SPOT NDVI images were used for time-period 1998-2008 to carry out the work on Kharif crops. The NDVI images were originated from VITO Image Processing and Archiving Centre, Belgium (www.vgt.vito.be). The SPOT NDVI images provide 10 days temporal and 1x1km spatial resolution. The images are available 23 days after the end of each dekad. The main advantage the usage of NDVI images is the continuous and timely availability. The NDVI image series contains complex information about the plant development, crop phenological stages and biomass accumulation. (Several other factors like the agronomic techniques as soil management/health, sowing procedures (flat/bed sowing) irrigation frequency/techniques, average farm size affect the NDVI value, too.) The satellite images are probably the best sources to collect current information about the phenological stages and development of different crops in Pakistan. One of the main problems, the possible errors in NDVI pixel values is due to cloud contamination or other errors in the measurements. The NDVI image processing is very important to reduce the pixel scale errors and increase the information content and determine value added derived variables. The work was started with the usage of two special agrometeorological NDVI image processing computer programs like VAST (Vegetation Analysis in Space and Time) and BAGS (Beginning of Agricultural Season) software. After determination the output images trial was made to discover the possible relationships between these images and district-wise statistical (official) yield of Kharif crops. It was impossible to determine a real useful relationship at the end. The work was continued with the direct usage of SPOT NDVI images by calculating NDVI sum and maximum values of the Kharif season for crop yield estimation. These NDVI derived variables – crop yield relationships proved to be relative weak for Pakistan. Decision was made to write a tailor made computer program for Pakistan to increase the accuracy of image processing and produce value added images with increased information content. This computer program could be considered as a combination of VAST and BAGS software. As a first step satellite images has been loaded for a year. The yearly time-series of each pixel of images was extracted as 36 dekadal value and this time-series vector was used for further calculations. The pixels corresponding water bodies (sea, lakes, rivers) were replaced by missing value. The image processing contained the following steps for each year and for decadal value vectors of each pixel:

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3.2.2 Smoothing The vector values were smoothed by the help of “4253H smoother, twice” statistical method. The “4253H smoother, twice” nonparametric smoother algorithm consists of a running median of 4, then 2, then 5, then 3, followed by Hanning process. The Hanning is a running weighted average with weights ¼, ½, ¼. The result of this smoothing method is then reroughed by computing residuals, applying the same smoother to them and adding the result to the smooth of the 1st pass. The smoothing process could be considered as a filtering process of less/not reliable values (Figure 3.2).

Figure 3.2:

Comparison of original (red) and smoothed (green) NDVI time-series for one location (pixel-wise) in Punjab (1998-1999)

3.2.3 Determination of main phenological stages Main characteristics of vegetation period for Kharif season were calculated from smoothed NDVI time series curve. Special attention was paid to determine the major features of cropping season (Starting, Peak and Ending Dekad) and the related main values (turning points) of the NDVI curve (Starting, Peak and Ending Value) with high accuracy. These values represent well the following main phenological events:  Starting Dekad = Date of emergence or planting (Beginning of measurable photosynthesis).  Peak Dekad = Date of maximum vegetative development, end of growing stage and beginning of flowering on the case of several crops (Time of maximum greenness or maximum photosynthesis level).  Ending Dekad = Date of senescence or harvest (Cessation of measurable photosynthesis). Any error in the determination of significant phenological phases later can escalate, resulting false relationships and to wreck the calculations. At the first sight it seems to be very simple and easy to identify the main phenological stages. Yes, theoretically this is an easy task. In

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the real world the satellite images contain pixels with mixed cropping patterns or uncultivated (wild) vegetation. In this case there are very strange and unexpected NDVI curves requiring right planned algorithm for exact automatic detection. 2-3 different conditions were used to determine the date and value of turning points of NDVI curves with high accuracy. The following preliminary preconceptions were created for the Pakistani satellite image cut-out to determine the main phenological events of the Kharif season crops:  Starting dekad o Timing is not earlier than the 10th dekad and not later than the 25th dekad of the year o The course of previous NDVI values are decreasing and the following NDVI values showing increasing trend o The initial NDVI increment is higher than 0,01/dekad  o o o

Peak dekad Date occurs minimum 3 dekads later than Starting dekad Timing must happen before the 34th dekad of the year Peak dekad has the highest NDVI value of the cycle

 Ending dekad o It happens minimum 3 dekads after Peak dekad o Timing is not later than the 36th dekad for Kharif crops (reaching the end of the year the program automatically terminates the cycle) o The course of previous NDVI values is decreasing and the following NDVI values have increasing trend or the course is flattened 3.2.4 Calculation Different outputs of this computer program were used to determine the best ones to incorporate into yield forecast models. Steepness proved to be one of most useful variable. The following derivative images were calculated: -

Starting Value Peak Value Ending Value Amplitude (Peak - Starting Value) Starting Dekad Peak Dekad Ending Dekad Length of the Season Length of Growth (Peak - Staring Dekad) Sum of NDVI value from Starting to Ending Dekad Other Integrated NDVI values as requested S-value (Steepness with 50%, 70%, 80% and 100% values were calculated. The explanation of S-value determination is presented on the Figure 3.3.)

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40

P E AK 35

30

50 % 25

S T E E P NE S S = S lope of B lue S eries 1

20

S eries 2

15

50 % 10

5

E ND ING VAL UE

S T AR T 0 1

2

Figure 3.3:

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

Schematic graph of Steepness (S-value) calculation with 50 %

3.2.5 Comparison Comparing the output of this program with the output of VAST and BAGS it seems to be more reliable and realistic in the case of Pakistan. It was found very good harmony between the identified crop patterns of the satellite images and the real cotton, sugarcane and rice area.

3.3

Meteorological Data for Kharif Season

The daily meteorological observations of Pakistan Meteorological Department were used in this scientific investigation. The work with the Kharif crops was focused mainly on Punjab and Sindh provinces. The average minimum distance between the stations is approximately 200 km. The distribution of stations is consistent and describes well the main agricultural areas of Sindh and Punjab. The data-base contains hours with sunshine, precipitation, maximum and minimum temperature, relative air humidity and wind-speed data. The sunshine duration data deemed to be very useful in the crop yield forecast since the radiation could be one of the most important limiting factor of crop production when the water supply is adequate. Daily maximum and minimum temperature were applied in the Hargreaves formula to fill the gaps in the calculated global radiation time-series. The relative humidity of air and the minimum temperature were used to naturalize some international cotton crop models in Pakistan. The spatial positions of the stations with daily meteorological data are shown on the following map (Figure 3.4).

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Figure 3.4: Spatial distribution of meteorological stations with daily data applied for Kharif crop yield forecast (underlined name is indicating the existence of sunshine duration values)

In this phase of work mainly the global radiation was used deriving form the sunshine data. 13 stations have sunshine duration data, too (Figure 3.4). The global radiation was calculated from the sunshine duration data for these places by the help of Glover and McCulloch equation which is one possible version of well known Angstrom formula:

H S  0.29  cos   0.52  H0 S0

(3)

where H represents the global radiation, Ho the extraterrestrial radiation, S the sunshine duration, So the maximum possible sunshine duration for a given dekad and  is the latitude of the location under study. The constants for Angstrom formula were taken from Pakistani scientific publication (Ahmad and Ulfat, 2004). The minimum and maximum temperature was applied through the Hargreaves formula to complete the days with lack of data. Hargreaves formula estimates the global radiation on the basis of daily temperature range using the maximum (Tmax) and minimum (Tmin) temperatures: H  H 0  k RS  Tmax  Tmin , (4) where Hargreaves coefficient kRS which is between 0.16 (inland stations far from the sea) and 0.19 for stations at the sea-side. The (Tmax - Tmin) difference is the daily temperature amplitude. The comparison of results of Angstrom and Hargreaves method indicated good harmony on dekadal time-scale.

16

3.3.1 Calculation of Potential Evapotranspiration The potential evapotranspiration (PET) was calculated using the Penman-Monteith method by the help of FAO computer program ET0Calc (Version 2.3, May 2007). The input data of ET0Calc computer program were the following: maximum and minimum temperature, global radiation calculated by Angstrom or Hargreaves formula (some of the meteorological stations have no or not continuous sunshine duration data), average wind-speed and the maximum and minimum relative air humidity of air. The calibration procedure was done the find the best method to calculate the PET having different input data. The dekadal PET was calculated for 7 Pakistani stations (Dera Ismail Khan, Faisalabad, Hyderabad, Islamabad, Jhelum, Lahore and Queta) having sunshine duration, minimum and maximum temperature, relative humidity of air, wind-speed data applying 3 different ways: 1) PET determined by Penman method using sunshine duration data 2) PET determined by Penman method using estimated Global Radiation values of Hargreaves formula 3) PET determined by Hargreaves method The Penman equation (method 1) was accepted as reference for further calculation. These values were compared with calculated PET values of method 2 and 3. PET calculated by Hargreaves (method 3) is in close relationship with Penman reference PET. The determination coefficient was higher than 0.85, but the interception of regression line was about 1 and the sloop was significantly lower than 1 (e.g. 0.78) for Pakistan. Having the “combined” method (2) the correlation coefficient is higher than 0.95 (sometime close to 0.99!) and the regression line is quite close to 1:1. The accuracy of this method is better than 5%. This method (2) for all stations has superior accuracy over pure Hargreaves PET method (3) and there was no need to calibrate it.

3.4

Fertilizer Data

The crop fertilization plays a key-role in the formation of agricultural production of Pakistan. The ascending yields are in strong relationship with the increasing nutrient supply. On the well irrigated areas the fertilizers supply - mainly nitrogen - is the limiting factor of crop production. It is clear that water consumption would be the most important factor for predicting yields. Unfortunately no appropriate agricultural statistics is available on crop-wise or district-wise irrigation water supply. Due to lack of data on irrigation there was no possibility to develop water balance-based agrometeorological yield forecast method/model. The actual consumption of fertilizers were taken as an indicator of water consumption, as fertilization is highly correlated to crop yield statistics and as fertilization is driven by water availability in practice. This is the reason because the fertilizer data is one of the main predictor variables for crop yield forecasting. For the wheat forecast only province-wise monthly fertilization data was available from the provincial Crop Reporting Services and yearly district-wise nutrient off-take data from National Fertilizer Development Centre (NFDC), Islamabad for 1992-2007 time-period in 2008 March and April. Strengthening the co-operation with the NFDC we got access in 2008 August to the district-wise monthly nitrogen, phosphorous, potash and total fertilizer/nutrient off-take (commercial retail) data from 1992 until 2008 and onward. In our calculations we used the nutrient supply instead of fertilizer data, because these data are more easily comparable and reliable. It is obvious that the district-wise nutrient off-take data are weakly related to the real district-wise nutrient supply in several cases and several places.

17

In the Rabi season the wheat is the main cultivated crop. In the Kharif season there are several crops and different cropping patterns in Pakistan. It would be very useful to know the district-wise distribution of fertilizer between different cultivated plants and the scheduling of fertilization in practice. Unfortunately the statistical data are hardly accessible regarding the distribution of fertilizers between the Kharif crops. It is very problematic to collect data because of the small average farm size. (Most of the farm fields in Pakistan are owned and cultivated by small farmers. According to Agricultural Census 2000, 95 percent of the farms in Pakistan are in the subsistence range of less then 10 hectares and on an average own 2 hectares per farm family.) The size of farm areas relates with intricate cropping pattern and farming practices, too. To override these problems statistical tools were used to identify the relationship between the nutrient off-take and statistical yield. The correlation coefficient was calculated for all possible monthly and longer sub-periods between the yield and nutrient off-take data in order to determine the significant periods of fertilization. Assumption was made that there are small or moderate differences between the fertilization practices of neighboring districts cultivating same crop. It was taken special attention to avoid fake relationships. The relationship was accepted to be real when the significant time-period was valid for some of neighboring districts having similar agro-technique and agricultural practice. The reliability of all relationships was checked against the agricultural practice and experiences. Using the nutrient off-take data the district-wise values are incomparable even sometimes in different magnitude. The only exception is the rice zone (e.g. Sialkot, Sheikhupura and Gujranwala districts) where the off-take values divided by the rice acreage are nicely comparable with the rice yield data Figure 3.5.

2200 2100

Rice Yield (kg/Ha)

2000 1900 1800 1700

2

R = 0,6795

1600 1500 1400 1300 1200 0

1

2

3

4

5

6

Nutrient Off-take June-July Figure 3.5: Nutrient off-take – rice yield relationship for Sialkot, Sheikhupura and Gujranwala districts (1998-2007)

18

The following general rules were found for nutrient off-take (supply) - yield relationship:  The significant period starts one or two months before the sowing or planting and finishes before the end of vegetative phase of cycle.  The most important characteristic is the nitrogen and/or total nutrient supply  Some cases the importance of phosphorous nutrient supply is obvious  Only very weak relationship was found with the potassium nutrient off-take. Due to the very high effect of fertilizer on yield it would be very useful to collect/have cropwise fertilization/ nutrient supply data to increase the accuracy of crop forecast methods. Last but not least, it is very important to take into the consideration the time-lag. The collection, transfer and preparation of fertilizer off-take data is a time consuming procedure. This process can take 2-3 even in the worst case 4 months. It means that in the best case the off-take data is available for yield forecasting expert 2-3 months later. Practically to produce a working crop yield forecasting method the fertilizer and nutrient data are accessible for the most important first half of the vegetative period of any crop.

3.5

Yield Data of Kharif Crops

3.5.1 Statistical Yield Data for Kharif Season Historical yield time-series are crucial for the crop-yield forecasting work. The accuracy of the statistical data determines the upper limit of precision of the yield forecasting procedure. District-wise cotton, sugarcane and rice yield data available for Punjab province from 1986/87 and for Sindh, Balochistan and NWFP provinces from 1995/96 onward. The length of the data-set is appropriate for the agrometeorological research The cross checking of the yield data series shows good data quality in the case of Punjab and NWFP provinces and acceptable data quality for Sindh and Balochistan. 3.5.2 Crop Cuttings Data For official yield estimation, crop cuttings are used as a standard sampling technique in Pakistan based on a well defined procedure, using random tables to determine the location of samples. These small plot harvesting yield values (crop cuttings) are used to extrapolate the yield at larger spatial-scale at provincial Crop Reporting Services (PCRSs). Crop cut estimates best represent the Biological Yield for the crop. There are significant losses which must be considered in determining the economic yield, which is a measure of crop production that is available to the market place. The CRS of Punjab provided cotton and sugarcane crop cuttings. Unfortunately there was no time to geographically identify the place of crop cuttings. This data-set has big potential to verify, modify and refine the yield forecasting methods in the future. We would like to underline that mainly irrigation, tube-well irrigation and crop-wise nutrient supply data would be crucial for conventional crop modeling and yield forecasting. Some additional special crop information (like sugar content of sugar-cane, number of balls of cotton plant etc.) could increase the accuracy and could help in the adaptation of crop models. The usage of SPOT-VGT satellite images with higher resolution probably could improve the accuracy crop forecasting methods.

19

4

IDENTIFICATION OF CROPS ON SPOT VGT & DEVELOPMENT OF CROP MASKS

Wheat crop yield/production forecast technology was based on its global presence on entire available agriculture covering both rainfed and irrigated available agriculture area. There was only need to make a wheat related mask which can separate out the agriculture land from the non agriculture area (cities and villages area, rivers, desert, mountains, range lands, saline and water logged area etc. After the development of agriculture mask (SAM-2008), wheat production forecast technology was finalized. Kharif seasons crops i.e., cotton, sugarcane and rice mainly, are entirely different from wheat crop (Rabi season). Cotton is indeterminate growing plant belonging to less efficient C3 photosynthesis plant category. This cotton is one of the most difficult plants for yield forecast modeling both in term of crop production and its final product. Main reasons are its susceptibility to insect pests and diseases and economic return to farmers in Pakistan. Economic return includes the government purchasing rate for cotton and upcoming wheat crop pricing. This causes the less preference to go for extra manual cotton picking and to ensure the in time cultivation of possible high profit crop by the farmers. Sugarcane is also one the major crop of Pakistan which fulfills the need of about 160 millions peoples for sugar and other related products. This belongs to more efficient C4 plant group with high water delta requirement and is annual crop in Pakistan. Whereas rice is water loving plant and is again a member of less efficient C3 group. These three major crops are grown mainly in particular regions in the provinces on Punjab and Sindh. Punjab contributes about more than 65 %, Sindh about 20 % and remaining originated from NWFP and Balochistan. There was a significant difference in the cropping pattern and their location on all the available cultivated area in Pakistan. Although all of these crops are localized in term of their productivity sites but they are found mixed even at a village scale. Top ten districts of cotton, sugarcane and rice at national levels are as follows (Table 4.1, 4.2 and 4.3).

Table 4.1 S.No. DISTRICT 1 2 3 4 5 6 7 8 9 10

BAHAWALPUR RAHIM YAR KHAN LODHRAN BAHAWALNAGAR MULTAN MUZAFFARGARH KHANEWAL VEHARI RAJANPUR SANGHAR

Top ten Cotton growing districts of Pakistan CROP

Area '000' ha

Yield (kg/ha)

Cotton Cotton Cotton Cotton Cotton Cotton Cotton Cotton Cotton Cotton

290.2 276.0 209.2 225.0 189.0 198.7 191.0 233.9 138.8 132.4

676 689 703 647 684 635 656 488 739 666

Production ('000' bales) 1153.4 1118.0 864.6 855.9 760.0 741.8 736.6 671.1 603.1 518.4

20

Table 4.2

Top ten Sugarcane growing districts of Pakistan

S.No. DISTRICT 1 2 3 4 5 6 7 8 9 10

CROP

RAHIM YAR KHAN FAISALABAD JHANG BADIN SARGODHA THATTA MUZAFFARGARH KASUR TOBA TEK SINGH TANDO MOHAMMAD KHAN Table 4.3

4.1

Yield (kg/ha)

95.9 125.1 113.3 59.8 69.6 41.0 45.3 52.2 46.6 34.7

64385 47940 44829 62023 46672 59656 53523 45197 49187 61723

Production ('000' tons) 6174.5 5997.3 5079.1 3709.0 3248.4 2445.9 2424.6 2359.3 2292.1 2141.8

Top ten Rice growing districts of Pakistan

S.No. DISTRICT 1 2 3 4 5 6 7 8 9 10

Sugarcane Sugarcane Sugarcane Sugarcane Sugarcane Sugarcane Sugarcane Sugarcane Sugarcane Sugarcane

Area '000' ha

GUJRANWALA SIALKOT LARKANA SHEIKHUPURA SHIKARPUR OKARA JACOBABAD JAFARABAD HAFIZABAD KAMBAR SHAHDAT KOT

CROP Rice Rice Rice Rice Rice Rice Rice Rice Rice Rice

Area '000' ha

Yield (kg/ha)

246.8 180.5 96.2 178.5 96.2 107.6 66.2 100.2 123.4 60.6

1974 1943 3409 1680 3011 2363 3452 2274 1810 3325

Production ('000' tons) 487.2 350.7 327.9 299.9 289.7 254.3 228.5 227.9 223.4 201.5

S-value concept

The method was designed by Ibrar-ul-Hassan Akhtar (SUPARCO) and Attila Bussay (FAO). It is implemented using standard FAO tools (AgroMetShell and WinDisp) and some ad-hoc software (NDVI smoothing, locating season beginning, NDVI peak date etc.) written by Attila Bussay. It uses 1 km resolution SPOT NDVI images for the current season. One of the main problems is the identification of the different crop-stands on the satellite images in the Kharif season. One synthetic variable named as S-value was elaborated to override this problem. This variable is the combination of crop related NDVI curve and the duration of crop specific phenological development stages. The S-value is related to the shape (geometrical features) of NDVI curve. For the current growing season, from pre-planting to the time of the forecast, prepare a series of time-smoothed NDVI images using standard filters, and determine starting and time of peak NDVI.

21

Figure 4.1

Calculation of S-value (steepness)

Define a parameter (p=0.7 in the Figure 4.1 above) and derive the slope (“steepness”) of the line joining B and intersection E corresponding to the of the decreasing NDVI curve the corresponding to p. The steepness (s) depends on p and the shape of the NDVI profile. For a crop specific value of p and a steepness range can be defined that corresponds to the area where a given crop is cultivated. For rice, p=1.0 and s= 0.04 to 0.06 dekad-1. The NDVI values of a given pixel could be the summation of different crops and uncultivated area. Naturally the S-value for a given pixel can be taken into consideration as mixed information originated from several crops and bare surfaces. The S-value concept is suitable to select the relative homogeneous and characterizing pixels of different crop stands. The Figure 4.2 below illustrates 2008 rice growing areas defined as above. This is in very good harmony with the real acreage of rice cultivation.

22

Figure 4.2: S-values are indicating the main rice producing area for 2000/01 Kharif season

As a test of validation, the Figure 4.3 shows the correlation (year: 2000; provinces: Punjab and Sindh) between the area under rice cultivation and the number of pixels falling into the 0.04 to 0.06 range.

300 f(x) = 0.0613x + 16.4708 R² = 0.5901

250

KHa

200 150 100 50 0 0

500

1000 1500 2000 2500 3000 3500 Pixel count

Figure 4.3:

District-wise relationship graph between total rice pixel and harvested area for 2000/01 Kharif season

The S-value concept is useful to identify one crop or differentiate two or more crop-stands on the satellite images.

23

Different p values (p=0.50, p=0.70, p=0.80 and p=1.00) was used for different crops. Calibrating this method against field observations could increase the accuracy. The range of steepness values can vary slightly from year to year. This method is appropriate to identify the cultivation place of selected crop. Using a very strict range of S-values it is possible to find relative “clear” pixels where the crop acreage can reach 50%, 60 or 70%. These “pure” pixels are the most useful for yield forecasting providing high information content regarding the selected crop.

4.2

Frequency distribution

Frequency distribution is based on a smooth curve where the horizontal x axis from left to right indicates the different possible values of a variable (0-255 value for NDVI). The vertical y axis from bottom to top measures frequency of how many times a particular value occurs. For example, the x axis might indicate the NDVI values and the y axis might indicate frequency of its occurrence. Highest value for frequency will be at the top the curve and lowest on both extremes, nearly 0 and extremely high (Figure 4.4).

Figure 4.4: A sample symmetric bell curve of frequency distribution

This frequency curve could display symmetry; that is, one half (the left side) is the mirror image of the other half (the right side). A bell-shaped or mound-shaped curve is also normal, giving it special properties.

24

Spatial frequency distribution involves the same concept of frequency distribution but it also takes into account the space and time impact. This frequency is based on a vector grid which calculates the values and its frequency on a given vector grid.

4.3

Spatial Frequency Distribution Analysis (SFDA)

The Spatial Frequency Distribution Analysis (SFDA) involves a concept of frequency distribution along with land use impact on satellite vegetation images. Vec tor grid based information is a sufficient tool to study the spatial frequency variation over the time period scale. To understand this SFD, we can consider the example of Gujranwala district. Gujranwala district is one of the main rice growing districts in Punjab as well as in Pakistan. This SFD is based on the S-value which shows the strength of the land use feature and its number of occurrence on the surface.

Figure 4.5:

Spatial Frequency Distribution (SFD) in Gujranwala during 1998

The Radar graphs were produced (Figure 4.5) to show the difference in agricultural crops both quantitavely and qualitatively. Agriculture crops performance during the Kharif season 1998/99 was weak with S-value between 0-90 and frequency range of 090. Whereas Kharif season 1999/2000 was stronger with S-value between 0-105 and frequency range of 0-140. This S-value represents the field crop health and even crop conditions in wider sense whether its weak, normal, better, well or excellent. Frequency range determines the occurrence of the crop health factor on the satellite vegetation image for a given season and year. But this S-value range related to crop is not spread over the whole value range. Actually S-value range contains the part of value related to main crops, natural vegetation, bare soil, barren soil, rivers along with bed, urban area features etc.

25

Figure 4.6:

Spatial Frequency Distribution in Gujranwala during 1999

Figure 4.7:

Spatial Frequency Distribution in Gujranwala during 2001

Agriculture crops performance during the Kharif season 2001/02 was abnormal with Svalue between 0-130 and frequency range of 0-80. Main reason was the spatial frequency contribution to the low S-values and high S-values. This causes imbalance in cropping pattern of the area and also crop health performance factor. Rice being a major crop would be on the area reduction side with very wide variation in crop

26

performance ranging from very low to very high yield. This can result into biased crop yield forecast/estimation based on crop field random selectivity method.

Figure 4.8:

Spatial Frequency Distribution in Gujranwala during 2001

Agriculture crops performance during the Kharif season 2007/08 was satisfactory with S-value between 0-100 and frequency range of 0-115. Main reason was the spatial frequency contribution to the medium S-values.

4.4

Crop related S-Value

This S-value relationship is the unique feature of the derived satellite vegetation images (SPOT VGT) which proved to be realistic and satisfactory for the possible loca lization of target crop to improve and strengthen the crop yield forecast system. This relationship was based on the hypothesis that at least 20 % of the area under the 1 km2 SPOT VGT image is under the target crop. This S-value does not depend on the strength of NDVI value which previously represents the vegetation performance on the earth surface. This S-value identify the seasonal (decade-wise) behaviour based pattern related to particular crop considering that all other crop are also contributing relate d behavior to that given pixel. Different agricultural zones were developed in relation to major crop growing area like Cotton, Sugarcane and Rice growing districts to analyze the hypothesis that we can identify the pixel to develop crop yield forecast models. These zones were as follows in Table 4.4. S.NO

CROP ZONE

PROVINCE

DISTRICTS

Bahawalnagar, Bahawalpur, Lodhran and Rahim Yar Khan 2. Sugarcane Punjab Faisalabad, Jhang, Sargodha and Toba Tek Singh 3. Rice Punjab Gujranwala, Sialkot, Sheikhupura and Nankana Sb. Table4.4: Selected Major Crop-wise Districts inside Agriculture Zone 1.

Cotton

Punjab

27

After calculating Spatial Frequency Distribution Function (SFDF) for all agriculture zones from SPOT VGT images, we plotted the results on graph which gave very clearly accepted over hypothesis. Cotton and sugarcane major growing zones clearly differentiate the crop pattern inside, Figure 4.9 and Figure 4.10.

Figure 4.9:

Crop Specific SFDF for Cotton and Sugarcane Zone in 1999

Figure 4.10:

Crop Specific SFDF for Cotton and Sugarcane Zone in 1999

Specific S-value related to crops was derived from Spatial Frequency Distribution Function, Table 4.5. Sugarcane and maize being efficient light use plants (C4) surprisingly showed

28

very low level S-value ranging from 0.01 to 0.20 NDVI units. (May be these low values are connected to the geometrical structure of leaves of sugarcane and maize crops.) Cotton has S-value of medium level i.e. 0.20 to 0.30 NDVI units whereas rice produces highest S-value of 0.30 to 0.45 NDVI units. S.No.

Crops

1 2 3 4

Sugarcane Maize Cotton Rice Table 4.5:

4.5

Low S-Value (NDVI Value) 0.01-0.04 0.10-0.12 0.15-0.20 0.25-0.30

Low S-Value (DN Value) 2.6-10.1 25.1-30.1 37.6-50.1 62.6-75.1

High S-Value (NDVI Value) 0.08-0.12 0.17-0.20 0.28-0.30 0.40-0.45

High S-Value (DN Value) 20.1-30.1 42.6-50.1 70.1-75.1 100.1-112.6

Crop Specific S-Value for Major growing crops in Pakistan

Development of Kharif Crop Mask

4.5.1 Vector Grid Generation Vector gird of 1 kilometer x 1 kilometer was generated through grid generation tool in Arc View Software. This only need the area of interest on which grid is to be generated like in our case area of interest was districts administrative boundary. Main aspect of this grid is that it can be comparable in resolution to SPOT Vegetation satellite images. 4.5.2 Zone development and Extraction of Statistics Different zones were developed for the grid generation keeping in view the limitation of the software and computer possible processing to avoid processing errors. This reduces the number of grid boxes over the processing surface or information layer. Punjab and Sindh provinces were divided in four and two different zones based on adjoining administrative boundaries, respectively. These zones are as follows in Table 4.6. S.No. Zone No. 1 Zone 1

Province Punjab

2

Zone 2

Punjab

3

Zone 3

Punjab

4

Zone 4

Punjab

5

Zone 5

Sindh

6

Zone 6

Sindh

Table 4.6:

Districts Gujranwala, Sialkot, Sheikhupura, Nankana sb and Narowal Mandi Baha uddin, Sargodha, Jhang, Faisalabad, Toba Tek Singh and Hafizabad Kasur, Okara, Vehari, Pakpattan, Khanewal and Bahawalnagar Multan, Lodhran, Muzaffargarh, Rahim Yar Khan, Bahawalpur, Rajanpur and Dera Ghazi Khan. Larkana, Shikarpur, Jacobabad, Ghotki, Khairpur and Dadu Badin, Hyderabad, Nawabshah, Sanghar and Thatta

List of grid zones with respective districts

29

Figure 4.11:

Generated 1 km2 grid over ZONE 1 districts

4.5.3 Final Crop Mask Finally the extracted pixel information was linked to vector grid to make a layer based database which was used to develop the crop related mask at zone level. This zone level crop specific mask were joined together to make national Kharif crop mask. Finally, three different crops (cotton, sugarcane and rice) related masks were produced at National level (Selected districts only) for Punjab and Sindh provinces, Figure 4.12.

30

Figure 4.12: Kharif Crops Mask for Cotton, Sugarcane and Rice in Pakistan

4.6

Strengths and Weakness

Main strength of this technique is that it can identify the crop pixel adequately on satellite vegetation images. An example is presented for cotton on Figure 4.13.This enables us to analyze the qualitative aspect of crop output in term of yield forecast/estimation and to correlate on a reasonable basis. Furthermore this technique is laid out on the scientific basis which will further give a new aspect to yield forecasting system. Main weakness is the cropping pattern difference throughout the agriculture area which directly and indirectly affects the spatial frequency distribution function (SFDF). This could be managed through in depth ground oriented information. Most important weakness is the quality of SPOT vegetation satellite image which are prone to atmospheric effects and its resolution of 1 square kilometer. This could be addressed through MODIS vegetation satellite images of 250 or 500 square meters.

31

Figure 4.13:

Identified cotton growing pixels for 2003 and 2007 years

32

5

CROP-WISE ZONING AND MODEL CALIBRATIONS

5.1

COTTON

5.1.1 Introduction Cotton is an oil crop, though grown mainly for its fiber. The fiber consists of long, fine, flattened and convoluted hairs called ‘lint’, which can be detached easily from the seed. The value and quality of the cotton variety depends on the fineness of the fiber as well as its length. The longer and finer the staple the better its quality, since it can be used to produce thinner and lighter textiles without knots or uneven surfaces. A single fiber is a little less in diameter than a human hair, and is measured in micronaires. Five different staple lengths are distinguished: short (less than 21 mm), medium (21-25 mm), medium long (26-28 mm), long (28-34 mm), and extra long (more than 35 mm). The majority of the world production (about 60 per cent) consists of medium long staple. Medium staple is around 18 percent and short staple a mere 3 per cent, produced almost exclusively in South Asia. Longer staple lengths (long and extra long), comprise around 18 per cent of the world production of cotton (during 1977-78 to 1981-82), and can only be grown in more or less ideal conditions regarding soil, water, temperature, and light. Pakistan is the fifth largest producer of cotton in the world, the third largest exporter of raw cotton, the fourth largest consumer of cotton, and the largest exporter of cotton yarn. 1.3 million farmers (out of a total of 5 million) cultivate cotton over 3 million hectares, covering 15 per cent of the cultivable area in the country. Cotton production supports Pakistan’s largest industrial sector, comprising some 400 textile mills, 7 million spindles, 27,000 looms in the mill sector (including 15,000 shuttleless looms), over 250,000 looms in the non-mill sector, 700 knitwear units, 4,000 garment units (with 200,000 sewing machines), 650 dyeing and finishing units (with finishing capacity of 1,150 million square meters per year), nearly 1,000 ginneries, 300 oil expellers, and 15,000 to 20,000 indigenous, small scale oil expellers. It is by any measure Pakistan’s most important economic sector. Not surprisingly, government policy has generally been used to maintain a stable and often relatively low domestic price of cotton, especially since 1986-87 through the imposition of export duties, in order to support domestic industry. 5.1.2 Zone-wise selected districts and their yield trend analyses Cotton crop is mostly grown in certain districts in Punjab and Sindh province due to favorable growing climate. This include especially the southern Punjab (Rahim Yar Khan, Bahawalpur, Multan, Bahawalnagar, Khanewal, Vehari, Lodhran and Muzaffargarh) and Northern/Central Sindh (Ghotki, Sukkur, Khairpur, Sanghar, Nawabshah, Matiari).

33

Figure 5.1:

Cotton yield average of main producer districts of Punjab province

Provincial Crop Reporting Services (PCRS) department are responsible for the collection of the sample data for district level crop yield during and at end of season. These PCRS give first yield estimate on opinion survey and second (Final) yield estimate through field crop sample based crop cuts data. Generally the yield reported from Punjab province for major growing cotton districts is on average between 500 to 700 kg/ha (1997/98 to 2007/08) whereas in Sindh province, the cotton yield is more stable over the same period Figure 5.1 & Figure 5.2, respectively.

Figure 5.2:

Cotton yield average of main producer districts of Sindh province

Time based yield trends were studied to explore the statistical behavior of cotton yields in major selected cotton districts in Punjab and Sindh provinces, Table 5.1. This analysis

34

showed that only 3 districts yield have more than 0.50 coefficient of determination out of 12 districts with 7 – 18 % error (50-110 kg) in prediction over the time scale of 1998-2007. All the intercept values were on very high negative values which weaken the final forecasted value as it decreases the logical relationship of independent and dependent variables inside the model. This clarified the instability of the any statistical method which only relies on time based regression models for yield at district level. Table 5.1:

Main features of temporal cotton yield trend (1997/98-2007/08)

Districts

Intercept

Slope

R-Value

R2-Value

Error in kg

Bahawalnagar

-24831.24

12.71

0.38

0.15

107.82

Average Yield* 612.36

Bahawalpur

-18478.42

9.57

0.33

0.11

95.19

686.18

13.87

Khanewal

-45149.2

22.87

0.64

0.41

95.37

642

14.85

Lodhran

-62520.05

31.57

0.73

0.53

103.71

688.55

15.06

Multan

-59039.15

29.8

0.76

0.57

90.37

620.45

14.56

Muzaffargarh

-49118.62

24.81

0.84

0.71

54.93

549.18

10

R. Y. Khan

-45824.33

23.21

0.59

0.35

110.18

640.27

17.21

Vehari

-24368.69

12.48

0.38

0.15

105.64

619.91

17.04

Ghotki

-23995.58

12.34

0.37

0.14

109

701.82

15.53

Hyderabad

-22299.35

11.49

0.49

0.24

72.37

705.45

10.26

Khairpur

-29415.44

15.04

0.51

0.26

87.89

687.36

12.79

Nawabshah

-15717.53

8.22

0.49

0.24

51.63

735.27

7.02

% Error 17.61

* Average yield was calculated from 1997/98 to 2007/08 season (kg/ha)

5.1.3 Cotton models There was a trial made to apply two different cotton models for Pakistan which were elaborated under different climatological and agro-ecological environment (Greece, Uzbekistan). Cotton model elaborated for Greece The first examined agrometeorological cotton crop model was elaborated in Greece by Leonidas Toulios et al. (2001) for cotton yield estimation. The basic principle of this model is relatively simple: according to Monteith (1977), there is a strong relationship between the cumulative radiation quantity absorbed by the foliage during the cultivation period and the biomass production. This model was based on the linear relationship between the interception efficiency of photosynthetically active radiation (PAR) and the normalized difference vegetation index (NDVI). The total dry matter was integrated through the vegetation period from the daily product of the photo-synthetically active radiation absorbed by the foliage, the interception efficiency, the conversion efficiency (  i ) in dry matter of PAR. The linear dependence between interception efficiency and NDVI of crop cover and NDVI of soil was applied to estimate the TDM total dry matter content of cotton above the ground: n t

TDM  a b  NDVI crop  NDVI soil   PAR ,

(5)

n 1

where a=1.25 is a constant which related to interception efficiency of the photosynthetically active radiation absorbed by the foliage.

35

The cotton yield was calculated by the help of HI Harvest index which is the ratio of biomass accumulated in the yield and amount of total dry biomass. The value of HI was taken equal to 0.36. The model involves indirectly the Si Stress index, that is an index of the crop growing situation and limiting factor like water stress, insufficient evapotranspiration etc. Assumption was made that the Stress index could be considered equal to 1, assuming that the crop growth conditions are the appropriate. The results of model simulation for Pakistan delivered cotton yields in appropriate quantity order, but the accuracy was lower than required. The model was unable to describe the yearly variability of the cotton yield time-series. The reason of this fact was probably neglecting the effect of stress factors due to the lack of information about the actual limiting factors. Cotton model elaborated for Uzbekistan The second tested cotton agrometeorological model was developed for Uzbekistan by Ruecker et al. (2007). This model was developed in accordance with the previously mentioned principle of Monteith (1977), too. The daily dry biomass increment was calculated of multiplication ε light-use efficiency, the PAR photosynthetically active radiation and FPAR fraction of PAR absorbed by the plants. The ε light-use efficiency was written as the product of ε’ the maximum light-use efficiency, Tmin daily minimum temperature and VPD vapor pressure deficit. This approach of light use efficiency can be considered as a stress index of heat and atmospheric drought. The final cotton yield was the result of the summation of all daily value of the vegetative season multiplied with the Hi Harvest index: (6) Y  Hi     Tmin  VPD  PAR  FPARt



This model proved more reliable for Pakistani cotton yield estimation than the first model, but the accuracy of forecast still was unable to fulfill the requested yield forecasting accuracy. Finally the customized methodology was developed for yield forecasting of cotton for Pakistan. This was based mainly on specialized factor of S-value and SFDF. 5.1.4 Zone-wise Models Yield forecasting models for cotton was not based on the districts due to many reasons. These mainly includes, short (ten years) time series of SPOT satellite vegetation images, unavailability of meteorological, crop specific irrigation & fertilizer data at district level to build up a dynamic and relatively stable yield forecasting models. Table 5.2: Cotton crop yield forecasting equations of four cotton producing districts

S.No. Zone

District

Equation

1

PZONE1

PakPattan

Y =- 140.78 + 988.21 * ΣNDVI (AUG-OCT) + 617.34 * S-Value

1

PZONE1

Vehari

Y =- 140.78 + 988.21 * ΣNDVI (AUG-OCT) + 617.34 * S-Value

2

PZONE2

Khanewal

Y = 40.33 + 7.06 * Total Nutrients + 1034.88 * ΣNDVI (AUG-OCT)

2

PZONE2

Lodhran

Y = 40.33 + 7.06 * Total Nutrients + 1034.88 * ΣNDVI (AUG-OCT)

2

PZONE2

Multan

Y = 40.33 + 7.06 * Total Nutrients + 1034.88 * ΣNDVI (AUG-OCT)

2

PZONE2

Muzaffargarh

Y = 40.33 + 7.06 * Total Nutrients + 1034.88 * ΣNDVI (AUG-OCT)

3

PZONE3

Bahawalnagar

Y = 381.17 + 120.96 * Phosphorus + 288.15 * ΣNDVI (JUL-SEP)

3

PZONE3

Bahawalpur

Y = 381.17 + 120.96 * Phosphorus + 288.15 * ΣNDVI (JUL-SEP)

3

PZONE3

Rahim Yar Khan

Y = 381.17 + 120.96 * Phosphorus + 288.15 * ΣNDVI (JUL-SEP)

4

SZONE

Ghotki

Y = 740.26 + 380.48 * Phosphorus - 736.6638 * ΣNDVI (AUG-OCT)

4

SZONE

Hyderabad

Y = 740.26 + 380.48 * Phosphorus - 736.6638 * ΣNDVI (AUG-OCT)

4

SZONE

Khairpur

Y = 740.26 + 380.48 * Phosphorus - 736.6638 * ΣNDVI (AUG-OCT)

4

SZONE

Nawabshah

Y = 740.26 + 380.48 * Phosphorus - 736.6638 * ΣNDVI (AUG-OCT)

36

Four different zones based on districts were localized in Punjab and Sindh provinces of Pakistan, Table 5.2. These zones were developed with a concept of their geographical location, cropping patterns and spatial relationships. Kharif crop based agriculture mask was used to extract the different NDVI images based information to build up a yield calibration matrix. These images mainly include, Peak, NDVI-sum (ΣNDVI) on different time scale for a season, steepness during growing season. Other main variables include fertilizer and meteorological data. These calibration matrices were refined through Cook’s distance technique which removes the outliers from the calibration. These matrices were statistically analyzed at zone level to build up a regression model to forecast yield at district level within zone. Final yield forecast model equations are shown in the Table 5.2. The forecasted cotton crop yields of 2008/09 Kharif season are shown in Appendix 1 for 13 main cotton producer districts of Pakistan.

5.2

Sugarcane

5.2.1 Introduction Sugarcane is an important industrial and cash crop in Pakistan and in many countries of the world. It is grown in tropical and sub-tropical regions of the world in a range of climates from hot dry environment near sea level to cool and moist environment at higher elevations. Besides sugar production, sugarcane produces numerous valuable byproducts like, alcohol used by pharmaceutical industry, ethanol used as a fuel, bagasse used for paper, and chip board manufacturing and press mud used as a rich source of organic matter and nutrients for crop production. It also forms essential item for industries like sugar, chip board, paper, barrages, confectionery, plastics, paints, synthetics, fiber, insecticides and detergents. Pakistan occupies an important position in sugarcane producing countries of the world. It ranks at the fifth position in cane acreage and production and almost 15th position in sugar production. Sugarcane is an important source of income and employment for the farming community of the country. Sugarcane production in the country has increased over time. Despite expansion in production over years, increase in the productivity per unit of area has been very low in Pakistan. The average sugarcane production in the country required static between 45-50 tons/ha, which is very much low compared to the cane production by other countries. The average yield of sugarcane in the world is around 60 metric tons/ha, while India and Egypt are getting around 66 tons and 105 tons/has, respectively. In this way, Egypt with highest cane yield in the world is getting about 142 per cent high-yield than Pakistan. India with almost similar soil and climatic conditions is obtaining about 53 per cent higher cane yield than Pakistan. As it is one of the cash crops of the country, therefore, efforts should be made to improve its productivity. As a result of these efforts, substantial improvement can take place in its yield. Improved seed production, quality control and distribution depend largely upon the availability of skilled and competent local manpower, which is presented in insufficient in most developing countries. 5.2.2 Zone-wise selected districts and their yield trend analyses Sugarcane crop is mostly grown in most of available agricultural area in Punjab, Sindh and NWFP province due to favorable growing climate. This include the especially the Central Punjab (Faisalabad, Jhang, Toba Tek Singh and Sargodha), Southern Punjab (Rahim Yar khan, Muzaffargarh and Vehari), Southern Sindh (Hyderabad, Tando Allah Yar, Tando Muhammad) and irrigated area of NWFP (Mardan, Charsaddah and Peshawar). Generally the yield reported from Punjab province for major growing sugar districts is on average between 45 to 55 tons/ha whereas in Sindh province, the yield 50 to 60 tons/ha during 1997/98 to 2007/08 Figure 5.3 & Figure 5.4 respectively.

37

Figure 5.3:

Sugarcane yield average of main producer districts of Punjab province

Figure 5.4:

Sugarcane yield average of main producer districts of Sindh province

38

Time based yield trends were studied to explore the statistical behavior of sugarcane yields in major selected growing districts in Punjab and Sindh provinces, Table 5.1. This analysis showed that only 5 districts yield have about or greater than 0.50 coefficient of determination out of 20 districts with 8 – 20 % error (5-10 tons) in prediction over the time scale of 19982007. There is also strange association of yield with the time scale. Two district in Punjab (Okara and Pakpattan) and one in Sindh (Badin) have a negative correlation of -0.28, -0.17 and -0.51, respectively. This means that the yield of sugarcane among these districts is on declining side and while for remaining either stagnant or increasing. Generally, the error of only time scale models could be within range of 5-35 % and 2-20 tons/ha. Table 5.3: Main features of temporal sugarcane yield trend (1997/98-2007/08) Intercept

Slope

Bahawalnagar

-1150.1

0.6

RValue 0.48

0.23

Error in (tons) 3.82

Average Yield* 43.06

Bahawalpur

-1808.05

0.93

0.71

0.51

5.52

50.76

10.87

Dera Ghazi Khan

-1878.23

0.96

0.75

0.56

10.43

48.09

21.7

Faisalabad

-777.32

0.41

0.7

0.49

4.07

49.82

8.17

Jhang

-1457.57

0.75

0.65

0.43

3.31

45.53

7.27

Kasur

50.26

0

0.53

0.29

2.85

46.44

6.14

Mandi Baha-ud-Din

-1177.92

0.61

0.57

0.32

2.95

42.07

7.02

Muzaffargarh

-3552.97

1.8

0.63

0.4

7.4

45.27

16.34

Okara

1485.11

-0.72

-0.28

0.08

2.53

48.78

5.18

Pakpattan

-1894.88

0.97

-0.17

0.03

4.05

47.35

8.56

Rahim Yar Khan

-5715.5

2.88

0.61

0.38

5.73

53.34

10.74

Sargodha

-960.08

0.5

0.37

0.14

4.06

43.4

9.36

Toba Tek Singh

-747.35

0.4

0.66

0.44

4.14

51.01

8.11

Vehari

-2411.29

1.23

0.57

0.33

8.55

52.88

16.17

Badin

2687.68

-1.31

-0.51

0.26

14.29

56.7

25.19

Hyderabad

4601.17

-2.27

0.31

0.09

16.94

51.66

32.79

Khairpur

-144.85

0.1

0.06

0

6.09

55.5

10.97

Nawab Shah

1206.49

-0.57

0.64

0.41

4.27

60.26

7.09

Sanghar

1719.15

-0.83

0.72

0.51

6.55

56.59

11.57

-199.22 0.13 0.73 0.53 9.65 53.16 * Average yield was calculated from 1997/98 to 2007/08 season (tons/ha)

18.15

Districts

Thatta

R2-Value

% Error 8.87

5.2.3 Zone-wise Models Yield forecasting models for sugarcane was based on the created zones instead of district due to many limitations for a dynamic and relatively stable yield forecasting models, mainly short time series of available data like: ten years SPOT satellite vegetation images, unavailability of meteorological, crop specific irrigation & fertilizer data at district level. Four different zones based on districts were generated in Punjab and Sindh provinces of Pakistan (Table 5.4). These zones were developed on the basis of similarity of their agroecological environment, agro-techniques, locations, cropping patterns, spatial relationships and their contribution to total production. Kharif crop based agriculture mask was used to extract the different NDVI images based information to build up a yield calibration matrix. These images mainly include: Peak NDVI, S-value (steepness) during growing season. Other main variables are the nutrient off-take and meteorological data. These calibration matrices were refined through Cook’s distance technique which removes the outliers from the calibration. These matrices were statistically analyzed at zone level to build up a

39

regression model to forecast yield at district level within zone. Final yield forecast model equation are below, Table 5.4. Table 5.4: Sugarcane crop yield forecasting equations of four sugarcane producing districts S.NO.

Zone

District

Equation

1

Major

Faisalabad

Y = 38143.09 + 371.93 * Total Nutrients + 100260.2644 * S-Value

1

Major

Jhang

Y = 38143.09 + 371.93 * Total Nutrients + 100260.2644 * S-Value

1

Major

Kasur

Y = 38143.09 + 371.93 * Total Nutrients + 100260.2644 * S-Value

1

Major

Rahim Yar Khan

Y = 38143.09 + 371.93 * Total Nutrients + 100260.2644 * S-Value

1

Major

Sargodha

Y = 38143.09 + 371.93 * Total Nutrients + 100260.2644 * S-Value

1

Major

Toba Tek Singh

Y = 38143.09 + 371.93 * Total Nutrients + 100260.2644 * S-Value

Zone

District

Equation

2

Medium

Bahawalnagar

Y = 31887.57 + 270.56 * Total Nutrients + 112150.28 * S-Value

2

Medium

Mandi Baha uddin

Y = 31887.57 + 270.56 * Total Nutrients + 112150.28 * S-Value

2

Medium

Muzaffargarh

Y = 31887.57 + 270.56 * Total Nutrients + 112150.28 * S-Value

Zone

District

Equation

3

Minor

Okara

Y = 36155.68 + 183910.38 * Peak Time + 105522.05 * S-Value

3

Minor

Pakpattan

Y = 36155.68 + 183910.38 * Peak Time + 105522.05 * S-Value

3

Minor

Vehari

Y = 36155.68 + 183910.38 * Peak Time + 105522.05 * S-Value

3

Minor

Bahawalpur

Y = 36155.68 + 183910.38 * Peak Time + 105522.05 * S-Value

3

Minor

Dera Ghazi Khan

Y = 36155.68 + 183910.38 * Peak Time + 105522.05 * S-Value

Zone

District

Equation

4

Sindh

Badin

Y = 36799.19 + 2061.51 * Total Nutrients + 22669.85 * NDVI

4

Sindh

Hyderabad

Y = 36799.19 + 2061.51 * Total Nutrients + 22669.85 * NDVI

4

Sindh

Khairpur

Y = 36799.19 + 2061.51 * Total Nutrients + 22669.85 * NDVI

4

Sindh

Nawabshah

Y = 36799.19 + 2061.51 * Total Nutrients + 22669.85 * NDVI

4

Sindh

Sanghar

Y = 36799.19 + 2061.51 * Total Nutrients + 22669.85 * NDVI

4

Sindh

Thatta

Y = 36799.19 + 2061.51 * Total Nutrients + 22669.85 * NDVI

The forecasted sugarcane yields of 2008/09 Kharif season are published in Appendix 2 for altogether 20 districts in Punjab and in Sindh.

5.3

RICE

5.3.1 Introduction “The finest rice is from Pakistan whatever the brand name.” Pakistan is the producer of the world’s finest long grained aromatic basmati rice. Basmati, the king of rice, is held in the highest regard world over. Among all the other varieties of rice, none have the distinctive long grains or the subtle aroma for which this grain is considered so special. This also justifies the premium this rice commands against all other rice of the world. It takes birth in the most fertile valleys and plains of Pakistan. It is harvested by hand with delicate care, aged to perfection and then processed. The result is an extra long, pearly white, delicate grain with an irresistible aroma and delectable taste. The name basmati originated from a Sanskrit word "BASH", which means smell. This rice has special features, which makes it's naturally long grain fragrant and delicious in taste. The legend says that this rice was meant to be consumed by maharajas (kings), maharanis (queens), princes and royal families. This unique rice is just one crop a year grown only in

40

northern India and Pakistan, the region known as old Punjab - the land of five rivers originating from Himalayas. 5.3.2 Types and Forms of Rice Super Kernel is a long grain rice with a slender kernel, four to five times longer that it's width. The grains are separate, light and fluffy when cooked, and mostly used for recipes such as biryani, which require rice of a distinct shape and texture. Basmati rice 385 is dry and separate when cooked, resulting in long, thin grains, since the long grain increases only in length when cooked. Brown rice is the least processed form of rice, as the kernels of rice have had only the hull removed. The light brown color of brown rice is caused by the presence of bran layers which are rich in minerals and vitamins, especially the B-complex group. With a natural aroma and flavor similar to that of roasted nuts or popcorn, it is chewier than white rice, and slightly more nutritious, but takes longer to cook. Brown rice may be eaten as is or milled into regular-milled white rice. Parboiled rice is rough rice that has gone through a steam-pressure process before milling. It is soaked, steamed, dried, and then milled to remove the outer hull. This procedure gelatinizes the starch in the grain, and is adopted at the mill in order to harden the grain, resulting in less breakage, thus ensuring a firmer, more separate grain. Parboiled rice is favored by consumers and chefs who desire extra fluffy and separate cooked rice. 5.3.3 Zone-wise selected districts and their yield trend analyses Rice crop is mostly grown in most of specific area in Punjab and Sindh provinces due to favorable growing climate specially the soil aspect. This include the especially the NorthEastern part of Punjab (Gujranwala, Sialkot, Narowal, Sheikhupura, Hafizabad, Nankana Saab) and North Western and South parts of Sindh (Jacobabad, Larkana, Shikarpur, Dadu, Badin and Thatta). Generally the yield reported from Punjab province for major growing rice districts is on average between 1500 to 1800 kg/ha whereas in Sindh province, the yield 2500 to 3200 kg/ha during 1997/98 to 2007/08 Figure 5.5 & Figure 5.6, respectively. Main reason for very significant difference between the rice yield of Punjab and Sindh provinces is the quality rice grown. In Punjab the most of growing rice is of Basmati variety which produces very fine quality rice but very low in quantity. Whereas, the rice grown in Sindh is of IRRI and hybrids type which yields almost twice as compared to Basmati rice in Punjab.

41

Figure 5.5:

Rice yield average of main producer districts of Punjab province

Figure 5.6:

Rice yield average of main producer districts of Sindh province

Time based yield trends were studied to explore the statistical behavior of rice yields in major selected growing districts in Punjab and Sindh provinces, Table 5.5. This analysis showed that yield have about or greater than 0.50…0.95 and 0…-0.15 coefficient of determination with 3 – 10% and 6 - 10% error (50 to 250 kg/ha) in prediction over the time scale of 1998-2007 for Punjab and Sindh, respectively. This means that the yield of rice among Punjab districts is on increasing side and while for Sindh districts, yield is somewhat torpid.

42

Table 5.5: Main features of temporal sugarcane yield trend (1997/98-2007/08) Districts

Intercept

Slope

RValue

R2Value

Error in kg

Average Yield

% Error

BAHAWALNAGAR

-60391.1

30.95

0.61

0.37

139.96

1561.73

8.96

JHANG

-112037

56.71

0.98

0.95

44.49

1494.36

2.98

D. G. KHAN

-86903.3

44.44

0.71

0.5

154.63

2058.27

7.51

GUJRANWALA

-73996.4

37.86

0.85

0.72

82.15

1806.64

4.55

HAFIZABAD

-41506.2

21.59

0.64

0.41

89.84

1718.82

5.23

SHEIKUPURA

-64276.8

32.9

0.89

0.79

58.95

1589

3.71

NAROWAL

-137732

69.45

0.93

0.86

99.61

1315.91

7.57

SIALKOT

-117525

59.5

0.96

0.92

62.36

1594.27

3.91

OKARA

-118756

60.26

0.86

0.73

126.8

1892.27

6.7

PAKPATTAN

-88165.3

45

0.72

0.52

152.47

1924.73

7.92

DADU

-35835.2

19.35

0.25

0.06

263.44

2912.64

9.04

1579.4

0.39

0.01

0

232.26

2362

9.83

JACOBABAD

-48230.3

25.66

0.37

0.14

222.86

3148.27

7.08

LARKANA

-42990.8

23.07

0.38

0.15

193.48

3200.82

6.04

SHIKARPUR

-431.27

1.77

0.03

0

229.74

3117.73

7.37

GHOTKI

5.3.4 Zone-wise Models Yield forecasting models for rice was also not based on the districts due to many limitations to build up a dynamic and relatively stable yield forecasting models, mainly short time series of available data like ten years SPOT satellite vegetation images, unavailability of meteorological, crop specific irrigation & fertilizer data at district level. Five different zones based on districts were localized in Punjab and Sindh provinces of Pakistan, Table 5.6. These zones were developed with a concept of their geographical location, rice type grown inside the zones, cropping pattern, spatial relationships and their contribution to total production. Kharif crop based agriculture mask was used to extract the different NDVI images based information to build up a yield calibration matrix. These images mainly include, Peak, NDVI-sum (ΣNDVI) on different time scale for a season, steepness during growing season. Other main variables include fertilizer and meteorological data. These calibration matrices were refined through Cook’s distance technique which removes the outliers from the calibration. These matrices were statistically analyzed at zone level to build up a regression model to forecast yield at district level within zone. Final yield forecast model equation are below, in Table 5.6. Table 5.6: Rice crop yield forecasting equations of five rice producing districts S.No.

Zone

District

Equation

1

PZONE1

GUJRANWALA

Y = 920.41+812.73* ΣNDVI (JUN-SEP)+83.77*Nitrogen

1

PZONE1

HAFIZABAD

Y = 920.41+812.73* ΣNDVI (JUN-SEP)+83.77*Nitrogen

1

PZONE1

SHEIKUPURA

Y = 920.41+812.73* ΣNDVI (JUN-SEP)+83.77*Nitrogen

Zone

District

Equation

2

PZONE2

NAROWAL

Y = -189.68+3657.41*ΣNDVI (AUG-OCT)+75.8229*Nitrogen

2

PZONE2

SIALKOT

Y = -189.68+3657.41*ΣNDVI (AUG-OCT)+75.8229*Nitrogen

Zone

District

Equation

PZONE3

OKARA

y = 21.78+2459.48*ΣNDVI(JUN-SEP)+60.57*Nitrogen

3

43

3

PZONE3

PAKPATTAN

y = 21.78+2459.48*ΣNDVI(JUN-SEP)+60.57*Nitrogen

Zone

District

Equation

4

PZONE4

BAHAWALNAGAR

Y = 2449.62+2353.21*ΣNDVI (JUL-SEP) -117.61*Start Date

4

PZONE4

JHANG

Y = 2449.62+2353.21*ΣNDVI (JUL-SEP) -117.61*Start Date

4

PZONE4

DERA GHAZI KHAN

Y = 2449.62+2353.21*ΣNDVI (JUL-SEP) -117.61*Start Date

Zone

District

Equation

5

SZONE

DADU

Y = 3269.95+12014.27*S-Value -8685.98* S-Value-139.47*Nitrogen

5

SZONE

GHOTKI

Y = 3269.95+12014.27*S-Value -8685.98* S-Value-139.47*Nitrogen

5

SZONE

JACOBABAD

Y = 3269.95+12014.27*S-Value -8685.98* S-Value-139.47*Nitrogen

5

SZONE

LARKANA

Y = 3269.95+12014.27*S-Value -8685.98* S-Value-139.47*Nitrogen

5

SZONE

SHIKARPUR

Y = 3269.95+12014.27*S-Value -8685.98* S-Value-139.47*Nitrogen

The forecasted rice yields of 2008/09 Kharif season are presented in Appendix 3 for 15 districts covering about two third of rice production.

5.4

Strengths and Weakness

These yield forecasting models are relatively stable as they can forecast yield extreme limit i.e., upper and lower critical limits of yield at a district level. Secondly these models are based on crop specific performance on identified satellite vegetation pixels which can partially removed the biasness of models which can involve the unrelated vegetation performance aspect. Moreover the zones based analyses made the time series enough continuous to identify outliers and to make model statistically reliable as models cannot be based on ten or less number of years. Availability of input data (fertilizer, irrigation etc.) in time and quality is questionable several times. We need to streamline the channels to ensure the availability good quality data for crop yield/production forecasting system. We have to opt for MODIS VGT data of 250 or 500 m2 resolution to improve this S-value concept for possible crop identification on satellite based vegetation images based on their contribution factor. Secondly, MODIS data will give more localized crop performance indicators than SPOT VGT of 1 km2. There is also need to improve the S-values concept for different crops by delineating to reduce the calibration errors. Time series for modeling is very short and needed to be overlooked to enlarge to develop more dynamic and reliable forecasting system.

44

6

SUMMARY

Statistical models were elaborated for yield forecasting of wheat, cotton, sugarcane and rice crop grown in Pakistan. The accuracy of yield and production forecast seems to be appropriate, since the statistical error proved to be limited in all case. These yield forecasting models are suitable for usage in the governmental planning process or provide the higher level of food security. Further work needed to refine and adopt these methods for all crop producer districts and provinces of Pakistan. Detailed agrometeorological data-base was built containing and involving data from several different sources. One of the main successes of UTF/PAK/101/PAK FAO project was the ability to strengthen the co-operation between the stake-holders (governmental and local/provincial organizations) increasing the extent of the capacities. New computer programs and methods were produced. S-value (Steepness) concept was worked out to identify the different crop-stands of Kharif season on the NDVI satellite images. The usage of Steepness values is very promising, but there is a need to continue this scientific work in the future, too. The experiences with Kharif crops later could be useful to improve the accuracy of wheat yield forecast. The further work can provide highly accurate yield forecast for crops of Rabi and Kharif season. The yield forecast work never ends because of the changing agro-techniques and the continuously developing agriculture. The yield function is valid maximum for consecutive 3 years but it is recommended to recalculate more frequently, let’s say on yearly basis. This project proved to be successful demonstrating the potential of state-of-art tools and methods in crop yield forecast and agricultural statistics. To keep the accumulated knowledge and experience in Pakistan it would be crucial to continue this project and to build up an operational working system.

45

7

LITERATURE

Ahmad F. and I. Ulfat (2004): Empirical Models for the Correlation of Monthly Average Daily Global Solar Radiation with Hours of Sunshine on a Horizontal Surface at Karachi, Pakistan, Turk J Phys, Vol. 28 (2004) , 301 - 307. Monteith J. L. (1977): Climate and efficiency of crop production in Britain. Philos, Trans. R. Soc. London, Ser. B 281: 277-294. Ruecker G. R., Z. Shi, M. Mueller, C. Conrad, N. Ibragimov,J. P. A. Lamers, C. Martius, G. Strunz, S.W. Dech (2007): Cotton yield estimation in Uzbekistan integrating MODIS, LANDSAT ETM+ and field data, ISPRS Archives XXXVI-8/W48 Workshop proceedings: Remote sensing support to crop yield forecast and area estimates, Ispra, Italy Toulios L., A. Tournaviti, G. Zerva and T. Karacostas (2001): Agrometeorological modeling for cotton yield estimation. 1st ECAM meeting, Budapest. http://agrometcost.bo.ibimet.cnr.it/fileadmin/cost718/reporitory/toulios.pdf

46

APPENDICES

47

Appendix 1 Yield Forecast for 2008/09 Kharif Season Yield forecast of cotton, sugarcane and rice crop for 2008/09 Kharif season was finalized by SUPARCO on 15th November 2009. Appendix 1 COTTON YIELD FORECAST 2008/09 S.No.

Province

Districts

Yield (kg/ha)

1

PUNJAB

PAKPATTAN

765.0

2

PUNJAB

VEHARI

720.5

3

PUNJAB

KHANEWAL

689.8

4

PUNJAB

LODHRAN

698.5

5

PUNJAB

MULTAN

676.0

6

PUNJAB

MUZAFFARGARH

598.5

7

PUNJAB

BAHAWALNAGAR

483.6

8

PUNJAB

BAHAWALPUR

492.2

9

PUNJAB

RAHIM YAR KHAN

475.4

10

SINDH

GHOTKI

529.1

11

SINDH

HYDERABAD

555.6

12

SINDH

KHAIRPUR

497.4

13

SINDH

NAWABSHAH

548.3

PROVINCE

COTTON YIELD (kg/ha)

S.E

% Error

PUNJAB

637.8

9.1

1.1

SINDH

550.1

22.2

2.1

PAKISTAN

624.2

26.3

1

Appendix 2 SUGARCANE YIELD FORECAST 2008/09 S.No.

Province

Districts

1

PUNJAB

FAISALABAD

2

PUNJAB

JHANG

52.7

3

PUNJAB

KASUR

52.1

4

PUNJAB

RAHIM YAR KHAN

59.1

5

PUNJAB

SARGODHA

47.9

6

PUNJAB

TOBA TEK SINGH

52.3

7

PUNJAB

BAHAWALNAGAR

44.8

8

PUNJAB

MANDI BAHUDDIN

40.4

9

PUNJAB

MUZAFFARGRAH

51.1

10

PUNJAB

OKARA

50.1

11

PUNJAB

PAKPATTAN

48.3

12

PUNJAB

VEHARI

46.9

13

PUNJAB

BAHAWALPUR

49.2

14

PUNJAB

DERA GHAZI KHAN

55.9

15

SINDH

BADIN

48.6

16

SINDH

HYDERABAD

52.5

17

SINDH

KHAIRPUR

56.0

18

SINDH

NAWABSHAH

57.9

19

SINDH

SANGHAR

57.1

20

SINDH

THATTA

44.8

Yield in tons/ha 51.2

SUGARCANE YIELD (tons/ha) 49.4

S.E

% Error

132.8

0.2

SINDH

52.4

1602.1

1.9

PAKISTAN

50.6

379.6

0.6

PROVINCE PUNJAB

50

Appendix 3 RICE YIELD FORECAST 2008/09 S.No.

Province

1

PUNJAB

BAHAWALNAGAR

1502.9

2

PUNJAB

JHANG

1538.7

3

PUNJAB

DERA GHAZI KHAN

1946.8

4

PUNJAB

GUJRANWALA

1813.6

5

PUNJAB

HAFIZABAD

1619.9

6

PUNJAB

SHEIKUPURA

1607.9

7

PUNJAB

NAROWAL

1382.6

8

PUNJAB

SIALKOT

1573.8

9

PUNJAB

OKARA

1720.6

10

PUNJAB

PAKPATTAN

1802.1

11

SINDH

DADU

3217.2

12

SINDH

GHOTKI

2325.8

13

SINDH

JACOBABAD

3228.4

14

SINDH

LARKANA

3316.2

15

SINDH

SHIKARPUR

3167.7

RICE YIELD (kg/ha)

S.E

% Error

PUNJAB

1620.8

14.0

0.6

SINDH

2938.4

64.6

1.8

PAKISTAN

1965.7

26.3

1

PROVINCE

Districts

Yield (kg/ha)

51

Appendix 4 PRODUCTION FORECAST FOR COTTON, SUGARCANE AND RICE FOR KHARIF SEASON OF 2008/09

--

PRODUCTION BY FCA (Million Bales) 2007/08 --

PRODUCTION BY FCA (Million Bales) 2008/09 9.16

1.95

--

--

2.80

10.46

11.32

11.27

AREA BY FCA (000 ha)

PRODUCTION 1 (Million tons)

PRODUCTION 2 (Million tons)

PRODUCTION 3 (Million tons)

673.79

33.29

34.76

--

11.65 PRODUCTION BY FCA (Million tons) 2007/08 --

12.06 PRODUCTION BY FCA (Million tons) 2008/09 31.82

PROVINCE

COTTON YIELD (kg/ha)

AREA BY FCA (M. ha)

PRODUCTION 1 (Million Bales)

PRODUCTION 2 (Million Bales)

PRODUCTION 3 (Million Bales)

PUNJAB

637.8

2.25

8.44

9.17

SINDH

550.1

0.56

1.81

PAKISTAN

624.2

2.85

PROVINCE

SUGARCANE YIELD (tons/ha)

PUNJAB

49.4

SINDH

52.4

265.14

13.89

12.38

--

--

15.46

PAKISTAN

50.6

1043.50

52.80

52.81

50.81

PROVINCE

RICE YIELD (kg/ha)

AREA BY FCA (000 ha)

PRODUCTION 1 (Million tons)

PRODUCTION 2 (Million tons)

PRODUCTION 3 (Million tons)

PUNJAB

1620.8

1954.42

3.17

2.81

--

63.91 PRODUCTION BY FCA (Million tons) 2007/08 --

52.07 PRODUCTION BY FCA (Million tons) 2008/09 3.77

SINDH

2938.4

712.52

2.09

1.69

--

--

2.19

5.73

4.99

5.80*

5.56

6.54

PAKISTAN 1965.7 2915.64 Production 1 is based on achieved FCA area Production 2 is based on last five year average area Production 3 is based on forecasted yield for Punjab

*Punjab and Sindh yield was used for National Forecast for Rice.

Appendix 5 PERCENT CONTRIBUTION OF SELECTED DISTRICTS (YIELD FORECASTED) TO NATIONAL LEVEL PRODUCTION ON TEN YEARS SCALE (1998-2007) Year

Cotton (%)

Sugarcane (%)

Rice (%)

1998

73.3

72.6

68.2

1999

75.6

70.2

67.7

2000

75.8

72.2

66.2

2001

75.6

75.4

66.6

2002

76.8

75

65.6

2003

76.2

75.5

67.5

2004

76.7

75

67.3

2005

77.5

78.5

67.3

2006

73.6

72.7

73.1

2007

72.6

71.4

67.7