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Understanding the reasons for the yield gap between potential and actual yield can provide insights for enhancing canola production by adapting measures for ensuring food security. The canola yield gap under different management practices (e.g. water, nitrogen, N- and sowing dates) was quantified using research trials that were conducted at on-station and historical data (1980–2016) and the CROPGRO-Canola model for Punjab, Pakistan. The integrated approach revealed that low inputs of N, the amount of irrigation, sowing date and the use of seeds from home stocks were the principal causes for a low yield. The CROPGRO-Canola model was able to simulate the canola yield from research trials (R2 = >0.90) and farm survey data (R2 = 0.63). The average yield gap between potential (YP), N-limited (YNL), water-limited (YWL), N- and water-limited (YNWL), and overall farmer field yield (YOFF) was 50, 46, 62 and 72%, respectively. The yield-gap with achievable yield (YA) for YNL, YWL, YNWL and YOFF was 34, 28, 49 and 63%, respectively. Overall, the results showed that a high canola yield for farmers’ fields can be obtained by selecting appropriate varieties and sowing dates with N rate of 120 kg/ha and efficient irrigation management. However, further studies are necessary to fully comprehend the underlying causes for the low actual yield and the high yield variability of farmers’ fields.

Flood computer modeling is one of the recent practices which is used for the prediction of the flood occurrence at different intervals of time. A study was conducted on Narai Drain Hayatabad, Peshawar, to produce flood plain maps by integrating Arc-GIS with the Hydraulic Modeling software HEC-RAS. The area in the vicinity of Narai Drain was inundated due to the catastrophic flood occurred in the year 2010, which caused huge damage to public property, livestock, communication, and infrastructure. In this study, the Narai Drain watershed was classified into three watersheds, namely Narai Drain Upper, Lower, and Regi Drain. GIS technology was used to delineate the watersheds while the discharges were computed using the WinTR-20 mode. Expected rainfall depths were obtained by using Log-Pearson type III Distribution for different return periods (i.e., 2, 5, 10, 25, 50, 100, 200). The area inundated by the flood was estimated by using HEC-RAS. These results were then incorporated in GIS to prepare flood inundation maps. Maximum inundation occurred in the sensitive area of Narai Drain Lower for a 10-year return period. The integrated modeling approach used in this study was found very useful to delineate the area vulnerable to flood with a good estimation of inundation depths at various discharge values. The results show that in the upper reach of the Narai Drain, most of the area lying near the flood zone was safe for a 100-year return period, while the lower reach was vulnerable even for a return period of 10 years.

In countries like Pakistan, whose economy greatly depends on agriculture and predominantly crop production, the estimation of crop yield before harvesting is very important. Remote sensing allows early estimation of crop yield before harvesting. The objective of the study is to evaluate the possibility of MODIS-derived vegetation indices using GIS and RS to estimate pre-harvest wheat yield in the Potohar region, Pakistan. Two MODIS products MOD15A2H and MOD13A1 for the period 2009–2018 were used for the derivation of LAI and indices. Wheat yield data of each district for the study period were obtained from the agriculture statistics of Pakistan. Model was run using 16-days composite MODIS vegetation indices as independent variable and crop yield data as the dependent variable. To check the ability and accuracy of the model RMSE, MAE and MBE were calculated. Overall, the percentage average difference between the actual and predicted yield was within −1.986%. Average RMSE and MAE values ranged from 34.28 to 76.50 kg/ha and 108.09 to 129.99 kg/ha, respectively. The MBE value ranged from 7.20 to 62.80 kg/ha. The results concluded that accurate wheat yield predication can be made almost 2 months before harvesting using geospatial techniques along with the statistical modeling approach.

Climate change has a significant impact on the intensity and spread of dengue outbreaks. The objective of this study is to assess the number of dengue transmission suitable days (DTSD) in Pakistan for the baseline (1976-2005) and future (2006-2035, 2041-2070, and 2071-2099) periods under Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios. Moreover, potential spatiotemporal shift and future hotspots of DTSD due to climate change were also identified. The analysis is based on fourteen CMIP5 models that have been downscaled and bias-corrected with quantile delta mapping technique, which addresses data stationarity constraints while preserving future climate signal. The results show a higher DTSD during the monsoon season in the baseline in the study area except for Sindh (SN) and South Punjab (SP). In future periods, there is a temporal shift (extension) towards pre- and post-monsoon. During the baseline period, the top ten hotspot cities with a higher frequency of DTSD are Karachi, Hyderabad, Sialkot, Jhelum, Lahore, Islamabad, Balakot, Peshawar, Kohat, and Faisalabad. However, as a result of climate change, there is an elevation-dependent shift in DTSD to high-altitude cities, e.g. in the 2020s, Kotli, Muzaffarabad, and Drosh; in the 2050s, Garhi Dopatta, Quetta, and Zhob; and in the 2080s, Chitral and Bunji. Karachi, Islamabad, and Balakot will remain highly vulnerable to dengue outbreaks for all the future periods of the twenty-first century. Our findings also indicate that DTSD would spread across Pakistan, particularly in areas where we have never seen dengue infections previously. The good news is that the DTSD in current hotspot cities is projected to decrease in the future due to climate change. There is also a temporal shift in the region during the post- and pre-monsoon season, which provides suitable breeding conditions for dengue mosquitos due to freshwater; therefore, local authorities need to take adaption and mitigation actions.

This study explores the relative changes and future projections of temperature and precipitation for baseline (1976–2005) and future (2006–2040, 2041–2070, and 2071–2100) periods for the Bajaur, Mohmand, and Khyber districts of Pakistan situated in the Hindu Kush region. Data from 14 GCMs (out of which five GCMs were selected based on evaluation and validation) and three RCMs were downscaled and bias corrected using the quantile delta mapping (QDM) method for GCMs and the best easy systemic (BES) method for RCMs. The future extremes were projected by using the standard indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). Uncertainty in the data was analysed by a probabilistic distribution function (PDF), boxplots, and standard deviation. For RCP4.5, the GCM and RCM projections show an increase in the average maximum temperature of 0.98 °C for 2006–2040, 1.89 °C and 2.04 °C for 2041–2070, and 2.25 °C and 2.56 °C for 2071–2100, respectively, while the increase is almost double for RCP8.5 during the last period (2071–2099) over the whole study area. The percent increase in precipitation over the whole study area from GCMs and RCMs for RCP4.5 is 10.00–17.00% and 21.14–34.47%, while for RCP8.5, it is 11.73–22.12% and 16.17–31.50%, respectively. In RCM projections for RCP4.5, the Khyber district will experience drier conditions in mid-century, while for GCMs (RCP4.5), the same conditions are projected through the end of the century. In terms of extreme events, warm temperature extremes and extreme precipitation events show an increasing trend accompanied by a decrease in cold extremes over all regions. Hence, it is concluded that warm and wet conditions are projected to prevail in all regions. However, GCM data depict less uncertainty compared with RCM data, and while models show less error for the baseline period, the error increases slightly for the future periods. Thus, this study provides research findings along with policy recommendations essential to combat the potential impacts of projected changes in the regional climate.

The Thal region of Punjab often experiences dry weather conditions with extreme variability in rainfall on a spatiotemporal scale during Rabi cropping season. The current study assesses the impacts of agricultural drought on wheat crops for 2000–2015. MOD13Q1 and CHIRPS data were used for identifying and assessing variation in agricultural drought patterns and severity. Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Stress Vegetation Index (STVI) and wheat crop yield anomalies were computed to characterize the gravity of drought across the Thal region. The results indicate that the wheat Rabi cropping seasons of the years 2000–2002 experienced extreme agricultural drought, with a spatial difference in severity level causing low and poor yield, while the years 2011 and 2014 were almost normal among all the years, leaving varied impacts on wheat yield. The combined agricultural risk map was generated by integrating the agricultural and meteorological droughts severity maps. The combined risk map generated using weighted overlay analysis of all the parameters indicate that the total Thal area can be classified into slight, moderate and no drought covering 28.12, 12.76, and 59.12% respectively of the total area. Hence an agricultural risk map would be extremely helpful as a tool to guide the decision-making process for monitoring drought risk on agricultural productivity.

Reducing greenhouse gas (GHG) emissions is a global concern after Paris Agreement (PA). Identification of GHG emission sources and accurate and precise estimation of the corresponding emissions is the first step to meet reduction targets under PA. Increasing share of agricultural emissions in the global concentration has raised concerns on this sector. Now, reducing agricultural emissions without compromising food security is a real challenge. The present study was aimed to provide the current emission profile of Pakistan’s agriculture, historical emission trends and future projections under agricultural growth scenarios according to prescribed guidelines of Intergovernmental Panel on Climate Change (IPCC) for national GHGs inventory development. In this study, GHG emissions were estimated using United Nations Framework Convention on Climate Change (UNFCCC) Non-Annex-I Inventory Software (NAIIS), version 1.3.2 as per prescribed Revised 1996 IPCC Guidelines. In these emission estimations, tier-1 approach (which employs default emission factors) was used in accordance with national circumstances and data availability in the country. The emissions baseline was projected for 2030 under business as usual (BAU), food security (FS) and enhanced consumption pattern (ECP) scenarios. Agriculture sector emitted 174.6 million tons (Mt) of carbon dioxide equivalent (CO2-equivalent) emissions, of which 89.8 Mt is methane (CH4) and 83.7 Mt is nitrous oxide (N2O). Carbon monoxide (CO) emissions were found to be 1.07 Mt of CO2-equivalent. Emission from agricultural soils constituted 45.5% of the total agricultural emissions followed by 45.1% from enteric fermentation and 6.5% from livestock manure management. The rest of 1.7% of the emissions were from rice cultivation followed by 1.1% from burning of crop residue. Historical emission trends showed that the agricultural emissions grew from 71.6 to 174.6 Mt of CO2-equivalent from 1994 to 2015, a 143.8% increase over the period of 21 years. Emissions baseline projections were found to be 271.9, 314.3 and 362.9 Mt tons of CO2-equivalent under BAU, FS and ECP scenarios, respectively.

The assessment of meltwater sourcing from the clean and debris-covered glaciers is scarce in High Mountain Asia (HMA). The melting rate varies with the debris cover thickness and glacier orientation. The present study quantifies glacier melting rate attributed to varying thickness of debris cover in the Karakoram. We observed daily melting rates by installing ablation stakes over debris-free and debris-covered ice during a field expedition. The stakes were installed on glacier surface with debris cover thickness ranges between 0.5 and 40 cm at selected experimental sites during the ablation period (September and October 2018) and (July to August 2019). We selected three glaciers including Ghulkin, Hinarchi, and Hoper facing east, south, and north, respectively to assess the role of glacier orientation on melting rates. We observed that the debris-free ice melts faster than the debris-covered ice. Intriguingly, a thin debris layer of 0.5 cm does not enhance melting compared to the clean ice which is inconsistent with the earlier studies. The melting rate decreases as the thickness of debris cover increases at all the three selected glaciers. Furthermore, south-facing glacier featured the highest melting (on average ~ 25% more). However, the north and east-facing glaciers revealed almost same melting rates. We observed that the average degree-day factors (DDF) slightly varies within a range of 0.58–0.73 and 0.55–0.68 cm °C−1 day−1 for debris-free and 0.5 cm debris-covered ice, respectively, however, DDF largely reduces to 0.13–0.25 cm °C−1 day−1 for 40 cm debris-covered ice. We suggest continuous physical glacier ablation observations for various debris cover throughout the ablation zone to better understand the role of debris on melting.

Pakistan receives huge amount of rainfall during summer monsoon season that provides water replenishment for transition periods, helps in maintaining natural and anthropogenic ecosystems, and increased crop productivity. In this changing world, shifts in summer monsoon onset in Pakistan have been observed that seems to affect the society in general. Therefore, it is vital to address these summer monsoon onset shifts to help policy makings and implementation. The study was carried out to analyse the spatio-temporal variability in summer monsoon onset in four objectively defined regions covering all Pakistan. A total of 35 meteorological stations spreading over four regions (i.e., northern, central east, central west, and southern) were taken in to account and shifts in summer monsoon onset have been calculated for the period of 1971–2010. The analysis is based on the observational data of daily precipitation from 20th Jun-20th July for 40 years. The onset for each year and mean onset for each decade has been calculated for all stations. The data was analysed for homogeneity, spatial and temporal variability of monsoon rainfall has been calculated for all four regions, and station wise monsoon onset has been discussed in detail. The temporal analysis shows that the onset of monsoon has shift towards earlier onset from first week of July to last week of June at most of the stations in which the investigation was carried out during the studied period. The spatial analysis shows that the amount of monsoon precipitation during the onset period has decreased after 1970’s in almost all regions. This variability in monsoon onset can have major impacts on rain fed agriculture and cultivation of crops like maize, soybean, rice and sugarcane etc. and will have to revisit the cropping calendar.

Climate change has become a global phenomenon having severe ramifications on socio-economic sectors such as agriculture, water resources, environment and health. The effects of changing climate are much more prominent on developing economies as compared to the implications on well-developed industrial powers. Pakistan is one of the struggling agricultural economies confronting the issues of food insecurity as a consequence of profound climatic conditions. Notable changes in climatic factors such as temperature can have a direct effect on Growing Degree Days (GDD) and may alter the growing season length (GSL). Growing season length is an important factor in ensuring that each crop developmental stage has a sufficient period for the transition to the next developmental stage. Lengthening or shortening of GSL can have dire threats to crop development and ultimately, production. This study has been conducted to assess the changes in GSL in response to the variability in daily maximum and minimum temperatures with a base temperature of 5°C across Northern, Central and Southern Pakistan. RCP 4.5 and 8.5 have shown an increase of 2°C and 5.4°C in minimum and maximum temperatures, respectively.

Long-term conservation tillage and straw incorporation are reported to improve the soil health, growth, and yield traits of crops; however, little is known regarding the optimal nitrogen (N) supply under conservation tillage with straw incorporation. The present study evaluated the effects of conservation tillage practices (ZTsas: zero tillage plus wheat straw on the soil surface as such, and MTsi: minimum tillage plus wheat straw incorporated) and different N application rates (50, 100, 150, and 200 kg ha−1) on the yield and quality traits of cotton and soil characteristics in a five-year field experiment. The results showed that ZTsas produced a higher number of bolls per plant, boll weight, seed cotton yield, 100-seed weight, ginning out-turn (GOT), fiber length, and strength than MTsi. Among different N application rates, the maximum number of bolls per plant, boll weight, seed cotton yield, GOT, 100-seed weight, fiber length, strength, and micronaire were recorded at 150 kg N ha−1. Averaged over the years, tillage × N revealed that ZTsas had a higher boll number plant−1, boll weight, 100-seed weight, GOT, fiber length, and strength with N application at 150 kg ha−1, as compared to other tillage systems. Based on the statistical results, there is no significant difference in total soil N and soil organic matter among different N rates. Further, compared to MTsi, ZTsas recorded higher soil organic matter (SOM, 8%), total soil N (TSN, 29%), water-stable aggregates (WSA, 8%), and mean weight diameter (MWD, 28.5%), particularly when the N application of 150 kg ha−1. The fiber fineness showed that ZTsas had no adverse impact on fiber fineness compared with MTsi. These results indicate that ZTsas with 150 kg N ha−1 may be the optimum and most sustainable approach to improve cotton yield and soil quality in the wheat–cotton system.