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The agriculture sector plays a crucial role in the economy of Pakistan, contributing significantly to the Gross Domestic Product (GDP) and the employment rate. However, this sector faces challenges such as climate change, water scarcity, and low productivity, which have a direct impact on agricultural commodity prices. Accurate forecasting of commodity prices is essential for farmers, traders, and policymakers to make informed decisions and improve economic outcomes. This paper explores the use of a big data framework for agricultural commodity price forecasting in Pakistan, using a historical dataset on commodity prices in various Pakistani cities from 2007 to 2022 and Apache Spark to preprocess and clean the data. Based on historical spinach prices in Vehari City, the machine learning models Auto-Regressive Moving Average (ARIMA), Random Forest, and Long-Short-Term Memory (LSTM) were applied to price trends, and their performance was compared using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and squared correlation coefficient (R²). LSTM outperformed ARIMA and Random Forest with a higher R² value of 0.8 and the lowest MAE of 125.29. Such predictions can help farmers to effectively plan crop cultivation and traders to make well-informed decisions. [ABSTRACT FROM AUTHOR]

Agricultural production prediction is a challenging task in the deep neural network field. This paper presents a novel approach to fruit production prediction using deep neural networks to build a fast and reliable prediction system for agricultural production. In this article, we have considered different types of fruit production data (apples, bananas, citrus, pears, grapes, and total fruits), analyzed this data, and predicted the future production of these fruits using deep neural networks. The data are taken from the National Bureau of Statistics of Pakistan and the production output of major fruits. We implemented 3 different methods to predict the data for future fruit production. The first method is Levenberg-Marquardt optimization (LM), which was 65.6% accurate; the second method is called scale conjugate gradient back propagation (SCG), which had an accuracy of 70.2%, and the third method, is Bayesian regularization back propagation (BR), which was 76.3% accurate. These methods to predict fruit production are applicable in developing countries because they can compare production with increasing populations and assist in making new policies to increase production. The estimated results reveal that the government of Pakistan needs to further increase fruit production and create better policies for farmers to improve their production. [ABSTRACT FROM AUTHOR]

• New improved drought forecast model incorporating eight synoptic climate drivers. • Multivariate decomposition splits variables into intrinsic mode functions (IMF). • Simulated Annealing (SA) approach selects pertinent IMFs to inform drought pattern. • Kernel Ridge Regression (KRR) algorithm used to design new hybrid drought model. • Hybrid model provides significant hydrological and water management implications. New and improved drought models based on the World Meteorological Organization approved Standardized Precipitation Index, principally at multiple timescale horizons, are providing significant benefits to the hydrological community, by its widespread acceptance in the sub-field of water resources management, sustainable water use and precision agriculture. In this research paper, the existing challenges faced by a drought forecasting model trained at multiple time-scales are resolved where a new multi-phase, multivariate empirical mode decomposition model integrated with simulated annealing and Kernel ridge regression algorithms (i.e., MEMD-SA-KRR) is designed to attain significantly accurate drought forecasts for 3 agricultural sites (i.e., Faisalabad, Islamabad and Jhelum, located in Pakistan). Utilizing the multi-scalar Standardized Precipitation Index (SPI) time series as a target variable for characterization of drought, twelve multivariate datasets (derived from statistically significant lagged combinations of precipitation, temperature & humidity), that are enriched with eight synoptic-scale climate mode indices and periodicity, are utilized in designing a new drought model. The study constructs a hybrid MEMD-SA-KRR model, where firstly, the data are partitioned into their respective training and testing subsets after creating historically lagged SPI at timescale (t – 1). Secondly, the MEMD algorithm is conditioned to demarcate multivariate climate indices from their training and testing sets, separately, into their decomposed intrinsic mode functions (IMFs) and residues. Thirdly, the SA method is employed to decide the most suitable IMFs. Finally, the KRR algorithm is applied to the selected IMFs to forecast multi-scaler SPI, at 1-, 3-, 6- and 12-monthly forecast horizon. The results are benchmarked with Random Forest, integrated with MEMD and SA to develop the MEMD-SA-RF equivalent model. The multi-phase MEMD-SA-KRR model is tested geographically in Pakistan, revealing that the MEMD-SA-KRR hybrid model generates reliable performance in forecasting multi-scaler SPI series, relative to the comparative models based on error analysis metrics. The hybrid drought model incorporating the most pertinent synoptic-scale climate drivers, as the model inputs has significant implications for hydrological applications and water resources management including its potential use in drought policy and drought recovery plans. [ABSTRACT FROM AUTHOR]

This study investigates the rice price dynamics and their impacts on demand and supply over the time in agricultural markets of Pakistan. Time series data on production and prices for the period 1984-2018 were used to study the cobweb phenomenon in local rice markets. Linear demand and supply models were used to examine the unstable rice-market. Price dynamics were examined through identifying the time path of difference equation derived from demand and supply functions. The results inferred that market prices of rice have remained unstable in agricultural markets of Pakistan over the history. Furthermore, rice prices exhibited damped oscillations towards stable market equilibrium over last three decades in Pakistan. It is suggested that the government may respond promptly to the instability of rice markets by formulating the appropriate agricultural policies, developing state-of-the-art storage facilities and ensuring the steady transmission of market information across the national as well as international rice markets. The study provides deep insights into understanding the on-going and future trends of price fluctuations with respect to demand and supply shocks in rice market of Pakistan. It would greatly help the stakeholders and policy makers in forecasting and stabilizing the rice markets, and alleviating the food insecurity. [ABSTRACT FROM AUTHOR]

This study was conducted in three tehsils, Gujranwala, Nowshera Virkan and Wazirabad of district Gujranwala. The major objective of the study was to explore the causes and consequences of the different conflicts faced by the rural people. A total 384 respondents were chosen at random. Data were collected through face-to-face interview technique on a structured, validated and reliable interview schedule from randomly selected 384 respondents. Collected data were analyzed with the help of Statistical Package for Social Sciences (SPSS). Findings indicated that, conflicts between two groups; conflicts over access to power, feeling of self-superiority, dispute over access to agricultural resources, mutual farming, disturbance in family relationship and theft of farm produce were the major causes of the conflicts as perceived by respondents. The conflicts had adverse impacts on the availability of labor, inputs, and farm assets and more importantly on adoption of innovations among farmers in particular. In result, decrease in farm production and income of farmers was more likely. Multiple linear regression analysis confirmed that, age, education, income level, land size and farming experience of respondents had statistically significant relationship (P<0.05) with the effects of conflicts. This implies that by strengthening the socio-economic profile of the rural people the severity of the conflicts can be minimized. This study urges the development and execution of conflict resolution strategies in the study area through the synergistic institutional coordination. It was recommended that extension staff can minimize the threat of conflicts by educating the farming community and they should conduct the seminar and training sessions for the local stakeholders who are the active part of conflicts resolution. [ABSTRACT FROM AUTHOR]

The current research was intended to investigate the access and control over farm input resources by the women farmer and their involvement in decision making in rural areas of Khyber Pakhtunkhwa (KP)-Pakistan. Three districts namely Bannu, Swabi and Swat from KP were purposively selected and a sample size of 384 women farmers were selected through multistage sampling technique. Descriptive analysis revealed that 305 women farmers were involved in rearing poultry. A 5 point Likert scale was used, showed that women farmers had various constraints out of which Pardah, mobility and lack of information source were ranked on top with Likert score value of 4.12, 4.00 and 3.94, respectively. Due to male dominant society, 62.8% of the decisions were taken by spouse. Results of Chi-square analysis showed a significant association for age and decision making which means with increasing age of the women, their involvement in decision making increases, majority of the rural women were either not taking interest or not given the opportunity of decision making process of farming and family matters. Rural women have less access to farm inputs and less income generating opportunities. It is concluded that non-availability of timely technical knowledge and extension services, low family income, less access to farm inputs and inadequate income generating opportunities were the major constraints. Women farmers should be given easy and quick opportunity to open bank account, credit facilities and agriculture inputs and extension services. [ABSTRACT FROM AUTHOR]

Timely and reliable estimation of regional crop yield is a vital component of food security assessment, especially in developing regions. The traditional crop forecasting methods need ample time and labor to collect and process field data to release official yield reports. Satellite remote sensing data is considered a cost-effective and accurate way of predicting crop yield at pixel-level. In this study, maximum Enhanced Vegetation Index (EVI) during the crop-growing season was integrated with Machine Learning Regression (MLR) models to estimate wheat and rice yields in Pakistan's Punjab province. Five MLR models were compared using a fivefold cross-validation method for their predictive accuracy. The study results revealed that the regression model based on the Gaussian process outperformed over other models. The best performing model attained coefficient of determination (R2), Root Mean Square Error (RMSE, t/ha), and Mean Absolute Error (MAE, t/ha) of 0.75, 0.281, and 0.236 for wheat; 0.68, 0.112, and 0.091 for rice, respectively. The proposed method made it feasible to predict wheat and rice 6–8 weeks before the harvest. The early prediction of crop yield and its spatial distribution in the region can help formulate efficient agricultural policies for sustainable social, environmental, and economic progress. [ABSTRACT FROM AUTHOR]

The purpose of this study was to measure and assess comparative study of technical and environmental efficiency of agriculture sector in South Asia using balanced panel data for the period 2002–2016. The translog stochastic frontier analysis approach was applied to estimate output-oriented technical efficiency and input-oriented environmental efficiency. Results of translog production model give that output elasticity w.r.t. land, labor, capital and fertilizer is 2.13, 1.26, 0.01 and 0.17, respectively. Log likelihood test shows that there is technical inefficiency in the agriculture sector of South Asian countries. The average value of output-oriented technical efficiency for South Asian countries was 0.92 ranging from 0.82 to 0.97. This suggests that agricultural production of South Asian region could be increased up to 8 percent by eliminating the effects of technical inefficiency. Moreover, the findings show that Sri Lanka is the most technically efficient country having efficiency 0.99 followed by India (0.98), Bhutan (0.93), Bangladesh (0.89), Nepal (0.88) and Pakistan (0.85). The results of input-oriented environmental efficiency score of South Asia were 0.77 ranging from 0.57 to 0.97. This shows that there is opportunity to enhance environmental efficiency of South Asian countries by 23%. Sri Lanka achieved the highest environmental efficiency (0.97) followed by India (0.96), Bhutan (0.72), Bangladesh (0.71), Pakistan (0.67) and Nepal (0.57). It is recommended that there must be collaboration among the South Asian countries in research and development especially in agriculture sector on priority bases. [ABSTRACT FROM AUTHOR]

The current study assessed the mechanization challenges and scenario prevailing in rural area of district Peshawar of Khyber Pakhtunkhwa province in Pakistan. This study employed a multi stage sampling technique to gather data on the socio-economic features of the farmers and available machinery. A total of 240 rural farmers were randomly selected from two local union councils of provincial government Khyber Pakhtunkhwa Peshawar. The accumulated data were analysed using mean and standard deviation with an acceptance mean value of =3.00 and estimating logit model. Socio demographic features revealed that majority (52%) were in middle age group of 41-50 years, 64% were literate, while 68% respondents had farming as their income source and land owners were 64% with small landholding (46%). The study exposed that illiteracy of the respondents (4.10), et al system (3.98), lack of trained machinery operators (4.27), access of roads to the farm (4.45), adequate capital (4.00) and costly inputs (3.80) were some of the challenges plaguing the use of agricultural mechanization in the rural area. It was established that agricultural mechanization has significant role in boosting farm productivity, improving farmers' livelihood, ensuring economic growth, availability of off-seasonal farm produce, increasing income generating opportunities and reduction in time of operation among others. Logistic Regression indicated a highly significant (p<0.01) positive effect of key determinants including; income source (0.008), farm size (0.001), farming experience (0.004), extension visits (0.009), access to credit (0.002) and access to agricultural machines (0.006) on farm mechanization adoption. The study emphasized that government may make agricultural mechanization and farming resources available and reachable to the farmers in order to persuade farmers in using them to maximize production, arrange awareness trainings and subsidize costly agricultural inputs. [ABSTRACT FROM AUTHOR]

In Pakistan, the Extension Field Staff (EFS) of public and private sectors uses various methods and strategies to educate the farmers about agricultural technologies. This study was attempted to understand the perception of farmers about various teaching methods used by the EFS of public and private sectors for the dissemination of agricultural information. The study was conducted in purposively selected two tehsils (Multan Saddar and Shujabad) of district Multan. A sample of 400 farmers was selected randomly and data were collected through a pretested and validated interview schedule. A paired t-test was also used to compare the teaching methods of both sectors. The t-value (5.37) shows a strong significant difference in farm and home visits as well as mean value also shows that private sectors' EFS (Mean= 2.46) paid more farm visits as compared to public sectors' EFS (Mean= 1.95). There were various extension workers of the private sector working in the study area so, they frequently visit farmers' field or homes one after one. Similarly, the private sector made greater use of group contact methods especially farmers' meetings to educate farmers than did the public sector. The overall ranking of the private sector regarding the effectiveness of extension teaching methods was relatively higher than the public sector. [ABSTRACT FROM AUTHOR]

Agriculture is the mainstay of Pakistan's economy and contributes 24 percent of the GDP. Considering its vital role, planners and policy makers are always keen to have timely forecasts for the important crops such as wheat, cotton, rice and sugarcane. Of these sugarcane is a major cash crop and an important source of income for farmers in Pakistan. The present study is focused on developing and estimating time series models to forecast sugarcane production in Pakistan. Box-Jenkin (1976) methodology was employed to estimate production forecasting model using annual time series data as available from Pakistan Bureau of Statistics (PBS) and various issues of Pakistan Economic Survey for the years 1947- 2017. An appropriate ARIMA (2. 1,1) model was estimated to forecast the production of sugarcane crop in Pakistan for the years 2018-2029. Over this period the model predicts a significant increase (6.56%) in sugarcane output. These forecasts can be very useful for agricultural policy makers, sugar industry as well as fanners in making prudent resource allocation and production decisions for sugarcane in Pakistan. [ABSTRACT FROM AUTHOR]

Major objective of this study was an overtime analysis of secondary data to calculate compound growth and forecast area, production and average yield in the Punjab Pakistan. Average yield of Cotton in the Punjab Pakistan. For this purpose, data collected from government reports for the period of 1990-2017 was criticaly examined. Empirical results indicated that area under cultivation showed negative compound growth rate while production and average yield showed positive compound growth in Pakistan. While, in Punjab, compound growth rate of area under cotton cultivation and production showed negative while average yield showed positive growth. Forecasting of cotton showed that in Pakistan, area, production and average yield will be on rise by 2025. In the Punjab, area of cotton cultivation will increase by 2025 but production and average yield of cotton will show consistent decline. As the Punjab is leading contributor in terms of cotton production, this decline will impact economy and livelihoods significantly. Therefore, study urge the need of cotton policy and holistic actions by government to subsidize inputs availability and conserve water and land. [ABSTRACT FROM AUTHOR]

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 about 37188 thousand tons and 3454 kg ha-1, respectively. The rice (production and yield) forecast for 2029-30 will be about 7176 thousand tons and 2630 kg ha-1, respectively. The maize (production and yield) forecast for 2029-30 will be about 5784 thousand tons and 6088 kg ha-1, respectively. In short, the production growth rate of wheat, rice and maize was higher in 2012. [ABSTRACT FROM AUTHOR]

Pakistan's economy is largely driven by agriculture, and wheat, mostly, stands out as its second most produced crop every year. On the other hand, the average consumption of wheat is steadily increasing as well, due to which its exports are not proportionally growing, thereby, threatening the country's economy in the years to come. This work focuses on developing an accurate wheat production forecasting model using the Long Short Term Memory (LSTM) neural networks, which are considered to be highly accurate for time series prediction. A data pre-processing smoothing mechanism, in conjunction with the LSTM based model, is used to further improve the prediction accuracy. A comparison of the proposed mechanism with a few existing models in literature is also given. The results verify that the proposed model achieves better performance in terms of forecasting, and reveal that while the wheat production will gradually increase in the next ten years, the production to consumption ratio will continue to fall and pose threats to the overall economy. Our proposed framework, therefore, may be used as guidelines for wheat production in particular, and is amenable to other crops as well, leading to sustainable agriculture development in general. [ABSTRACT FROM AUTHOR]