Sajid Ali1, Nouman Badar1, Hina Fatima2
E-mail | sajid_economist@yahoo.com
...verage (ARIMA) models of forecasting. Using data for 1948 to 2012, productions and yields of both crops were forecasted for 18 years starting from 2013 to 2030. ARMA (1, 4), ARMA (1, 1) and ARMA (0, 1) were found appropriate for sugarcane production, sugarcane yield, and cotton production respectively, whereas ARIMA (2, 1, 1) was the suitable model for forecasting cotton yield. Some diagnostic tests were also performed on fi...
Dhananjoy Mandal 2K. Baral 3M. K. Dasgupta
Developing site-specific appropriate precision agriculture
...ogy, agrometeorology and forecasting inputs and outputs by marketing, production, protection and processing, and other essential information provided through a decision support system (DSS), in an agriinformatics networking such that is not ordinarily available to Indian farmers in general. Due to Precision Farming (PF), production increased by 40 to 60 percent farmers’ margins of the produce and reduction of the commission charged by the middlemen to 7-...
Dilshad Ahmad*, Muhammad Irfan Chani and Asad Afzal Humayon
Hassan Hashim Ghalib, Syed Attaullah Shah*, Abbas Ullan Jan and Ghaffar Ali
Saleem Abid1*, Nasir Jamal2, Muhammad Zubair Anwar3 and Saleem Zahid4
...ere used. Five different forecasting models such as Linear trend model, Quadratic trend model, Exponential growth model, S-curve trend model and double exponential smoothing model were used to find the best fitted model for area and production of potato in Pakistan. Forecasting errors namely mean absolute percentage error (MAPE), mean absolute deviation (MAD) and mean squared deviation (MSD) were used as model selection crit...
Saima Rani and Irum Raza*
COMPARISON OF TREND ANALYSIS AND DOUBLE EXPONENTIAL SMOOTHING METHODS FOR PRICE ESTIMATION OF MAJOR PULSES IN PAKISTAN
...ned to find out suitable forecasting method among the two forecasting methods namely trend analysis and double exponential smoothing. Measures of accuracy (MAPE, MAD and MSD) were used as the model selection criteria that could best describe the trend of prices of major pulses such as gram, mash, masoor and mung during 1975-76 and 2009-10. Double exponential smoothing method was found to be pertinent for price estimation of ...
Nouman Badar*, Hina Fatima**, Abdul Jabbar*** and Muhammad Asif*
MAJOR FOOD CROPS PRODUCTION AND YIELD FORECAST IN PAKISTAN
...;">This study focuses on forecasting the production and yield of food crops in Pakistan. Utilizing the “Autoregressive Integrated Moving Average” (ARIMA) model and data from 1948 to 2011. The data was obtained from Ministry of Food and Agriculture, Islamabad and various Economic Surveys of Pakistan. The result predicts that wheat (production and yield) forecast for 2029-30 to be -1 about 37188 thousand tons and 3454 kg ha , respectively. The rice (...
Saqib Shakeel Abbasi*, Ayesha Tahir**, Irum Raza* Saleem Abid* and Muhammad Nisar Khan*
TREND ANALYSIS AND FORECASTING OF WHEAT AND RICE IN PAKISTAN
...en acquired and then the forecasting was made for the best fitted model with minimum error. Five year average prices for the individual crop(s) were also calculated to observe the past trend. The study demonstrates that for wheat and rice (Basmati and IRRI); S-Curve model is recommended for forecasting price. The study presents an insight to national policy makers regarding the essential crops and provides them with a refere...
Waqar Islam*
...gle and disease epidemic forecasting is very important. So here we have briefly introduced plant disease epidemiology through highlighting its various types by giving important examples. We further have explained the plant disease triangle and disease forecasting systems via inclusion of various models being utilized worldwide.
... 
Farhan Ahmad1,*, Muhammad Waris Sanjrani1, Shah Nawaz Khuhro1, Asif Sajjad2, Abid Ali3, Rashad Rasool Khan3, Farooq Ahmed3 and Junhe Liu4,*
...this study could help in forecasting and monitoring of whitefly incidence and its parasitism.
...
Saleem Ashraf1*, Ashiq Hussain Sangi1Zakaria Yousaf Hassan3 and Muhammad Luqman2
Aftab Khan, Shahid Ali, Syed Attaullah Shah and Muhammad Fayaz
...s. Zone wise analysis of forecasting effects of temperature shows that increase in temperature by 1 °C in 2040-2050 and by 2 °C in 2060-2080 will significantly increase net revenue of maize growers in Northern and Eastern zones, but insignificantly in Central zone. An increase in temperature by 1 °C in 2040-2050 and by 2 °C in 2060-2080 will decrease net revenue in Southern zone. Government needs to encourage research institutes for developing ...
Jhan Zeb, Muhammad Javed
Forecasting percentage contribution of plankton biomass towards increase in fish yield under composite culture conditions
Muhammad Amin1, Aftab Ahmad Khan2, Abida Perveen1, Zareen Rauf1, Sher Shah Hassan2*, Muhammad Arif Goheer2 and Muhammad Ijaz2
...line for delineation and forecasting of drought years in Pakistan. ...
Faheem Aslam1*, Aneel Salman1 and Inayatullah Jan2
Qaisar Mehmood1*, Maqbool Hussain Sial1, Saira Sharif1, Abid Hussain2, Muhammad Riaz3 and Nargis Shaheen4
... in Pakistan. Therefore, forecasting the production of fish is important for better production and for planning of fish export. Objective of this research is to propose suitable Autoregressive Integrated Moving Average (ARIMA) model for forecasting the production of fisheries, using Box-Jenkins’s (1976) methodology. Secondary data, “50 years of Pakistan: volume-iii (1947-1997)” published by Pakistan Bureau ...
Dilawar Khan1*, Arif Ullah2, Zainab Bibi1, Ihsan Ullah1, Muhammad Zulfiqar4 and Zafir Ullah Khan3
Samreen Fatima1*, Mudassir Uddin1
COMPARISON OF ASYMMETRIC GARCH MODELS WITH ARTIFICIAL NEURAL NETWORK FOR STOCK MARKETS PREDICTION, A CASE STUDY
... This study compares the forecasting performance
and also investigates more volatile stock markets using Asymmetric GARCH (A-GARCH) models and non-parametric
method (Artificial Neural Networks). In the A-GARCH; EGARCH and PGARCH models are used. Firstly, suitable
Asymmetric GARCH (A-GARCH) model was developed for forecasting and investigating leverage effect. Secondly,
an Artificial Neural Network...
and also investigates more volatile stock markets using Asymmetric GARCH (A-GARCH) models and non-parametric
method (Artificial Neural Networks). In the A-GARCH; EGARCH and PGARCH models are used. Firstly, suitable
Asymmetric GARCH (A-GARCH) model was developed for forecasting and investigating leverage effect. Secondly,
an Artificial Neural Network...
Kadir Karakuş1, Turgut Aygün2, Şenol Çelik3, Mohammad Masood Tariq4*, Muhammad Ali4, Majed Rafeeq4 and Farhat Abbas Bukhari4
...ditions and to build the forecasting model of live weight in lambs using 4 data mining CHAID, Exhaustive CHAID, CART, and multivariate adaptive regression splines (MARS). Effects of genotype and some environment factors such as birth type, dam age, lamb’ age in control and live weight on testis characteristics were also researched. The furthest importance sequence was obtained for testis length (TESLENG) (100%), followed by age, testis diameter (TDIA), a...
Yonghua Liu*, Xianhua Li, Xiongfei Yan and Gang Li
...e an important basis for forecasting and integrated management of A. fimbriana.
...
Mehar Ul Nissa Rais1*, Tahmina Mangan1, Jam Ghulam Murtaza Sahito1 and Naeem Ahmed Qureshi2
A Trend Analysis: Forecasting Growth Performance of Production and Export of Chilli in Pakistan
...t growth performance and forecasting of chilli in Pakistan. Annual time series data of 38 years (1981-2018) of chilli production and export was employed for this study. Overall, chilli production of the Pakistan exhibited a positive growth of 1 percent over the time; however, in Pakistan negative growth was recorded during 2001-02 and 2011-12, these losses can be attributed to major floods, diseases attack, poor management practices and shortage of high yieldi...
Muhammad Waqas1*, Muhammad Shoaib2, Muhammad Saifullah1, Adila Naseem4, Sarfraz Hashim1, Farrukh Ehsan1, Irfan Ali3 and Alamgir Khan1
...gn: justify;">Streamflow forecasting is a crucial hydrological variable. In the current study, the Artificial Intelligence (AI) based techniques: TB (Tree Boost), DTF Decision Tree Forest, SDT Single Decision Tree and conventional Multilayer Perceptron Neural Networks (MLPNN) are used for predicting streamflow of Jhelum River basin. The dataset was divided into two sections, i.e., training dataset (1971-2000); and testing dataset (2001-12). The tendency invest...
Muhammad Nasir1, Muhammad Usman Asif2*, Abdul Hayee Abid1, Qurat ul Ain Haneef1 and Muhammad Awais2
... pest managers as a pest forecasting tool for initiating management strategies at appropriate time during the cotton season....
Jing Yu1*, Yao Lu1,2, Zhaojin Lin1, Pimao Chen1 and Yuting Feng1,3
...based on GMM and MINM in forecasting spawning grounds in the Western Guangdong Waters.
...
Syed Ismat Hussain1, Khalid Mehmood2, Mudassar Khaliq3, Habib Anwar1, Syed Muhammad Zaka4, Ateeq ur Rehman5*, Muhammad Shahid6, Syed Atif Hasan Naqvi5*, Ummad ud Din Umar5 and Muhammad Asif Zulfiqar7
...n of degree days for its forecasting was calculated from 1st January by using the metrological data and base line temperature and found 7 generations of pink boll worm to be found in all districts. Data for moths trapped in sex pheromone traps was noted as highly significant i.e., P>0.0001. Peaks of moth catches in 14 districts were observed in the month of September to October while for pest survey P= 2 X 10-16 were highly significant meaning that all vari...
Zahid Iqbal*, Farhat Ullah Khan and Jalal-ud-Din
...nearity condition before forecasting. The forecasted values for the year 2016-17 to 2021-22 indicate an increasing trend in future of rapeseed and mustard production in Pakistan.
... 
Asmaa A. Badr, Eman A. Abo Elfadl, Mohammed M. Fouda, Sayed M. Elsayed
...from 2022 to 2033. ARIMA forecasting results showed that milk production will be increased in 2022 and 2023 for Holstein Friesian farm. Meanwhile, milk production will be increased in 2022 and will be steadily increased for the following years in Holstein German farm. The results also indicated that ARIMA (2,1,2) is the best fit model for Holstein Friesian in the first farm. Meanwhile, the ARIMA (0,1,2) is the best model for Holstein German in the second farm....
Samreen Fatima* and Muddasir-Uddin
...over, enhancement in the forecasting performance by combining the GARCH-M and ANNs (hybrid model), developed GARCH-M-ANNs model could be seen clearly. As per Akaike Information Criterion (AIC) and Schwarz Bayesian Information Criterion (SBIC), the study shows that GARCH (1, 1)-M estimations by changing conditional mean equations are found to be the most appropriate model. The three measures criterion namely: root mean squares error (RMSE), mean absolute error ...
