Agricultural production is under threat due to climate change in food insecure regions, especially in Asian countries. Various climate-driven extremes, i.e., drought, heat waves, erratic and intense rainfall patterns, storms, floods, and emerging insect pests have adversely affected the livelihood of the farmers. Future climatic predictions showed a significant increase in temperature, and erratic rainfall with higher intensity while variability exists in climatic patterns for climate extremes prediction. For mid-century (2040–2069), it is projected that there will be a rise of 2.8°C in maximum temperature and a 2.2°C in minimum temperature in Pakistan. To respond to the adverse effects of climate change scenarios, there is a need to optimize the climate-smart and resilient agricultural practices and technology for sustainable productivity. Therefore, a case study was carried out to quantify climate change effects on rice and wheat crops and to develop adaptation strategies for the rice-wheat cropping system during the mid-century (2040–2069) as these two crops have significant contributions to food production. For the quantification of adverse impacts of climate change in farmer fields, a multidisciplinary approach consisted of five climate models (GCMs), two crop models (DSSAT and APSIM) and an economic model [Trade-off Analysis, Minimum Data Model Approach (TOAMD)] was used in this case study. DSSAT predicted that there would be a yield reduction of 15.2% in rice and 14.1% in wheat and APSIM showed that there would be a yield reduction of 17.2% in rice and 12% in wheat. Adaptation technology, by modification in crop management like sowing time and density, nitrogen, and irrigation application have the potential to enhance the overall productivity and profitability of the rice-wheat cropping system under climate change scenarios. Moreover, this paper reviews current literature regarding adverse climate change impacts on agricultural productivity, associated main issues, challenges, and opportunities for sustainable productivity of agriculture to ensure food security in Asia. Flowing opportunities such as altering sowing time and planting density of crops, crop rotation with legumes, agroforestry, mixed livestock systems, climate resilient plants, livestock and fish breeds, farming of monogastric livestock, early warning systems and decision support systems, carbon sequestration, climate, water, energy, and soil smart technologies, and promotion of biodiversity have the potential to reduce the negative effects of climate change.

Information on soil erosion and related sedimentation processes are very important for natural resource management and sustainable farming. Plenty of models are available for studying soil erosion but only a few are suitable for dynamic soil erosion assessments at the field-scale. To date, there are no field-scale dynamic models available considering complex agricultural systems for the simulation of soil erosion. We conducted a review of 51 different models evaluated based on their representation of the processes of soil erosion by water. Secondly, we consider their suitability for assessing soil erosion for more complex field designs, such as patch cropping, strip cropping and agroforestry (alley-cropping systems) and other land management practices. Several models allow daily soil erosion assessments at the sub-field scale, such as EPIC, PERFECT, GUEST, EPM, TCRP, SLEMSA, APSIM, RillGrow, WaNuLCAS, SCUAF, and CREAMS. However, further model development is needed with respect to the interaction of components, i.e., rainfall intensity, overland flow, crop cover, and their scaling limitations. A particular shortcoming of most of the existing field scale models is their one-dimensional nature. We further suggest that platforms with modular structure, such as SIMPLACE and APSIM, offer the possibility to integrate soil erosion as a separate module/component and link to GIS capabilities, and are more flexible to simulate fluxes of matter in the 2D/3D dimensions. Since models operating at daily scales often do not consider a horizontal transfer of matter, such modeling platforms can link erosion components with other environmental components to provide robust estimations of the three-dimensional fluxes and sedimentation processes occurring during soil erosion events.

Crop cultivation provides ecosystem services on increasingly large fields. However, the effects of in-field spatial heterogeneity on crop yields, in particular triticale, have rarely been considered. The study assess the effects of in-field soil heterogeneity and elevation on triticale grown in an intensively cropped hummocky landscape. The field was classified into three soil classes: C1, C2, and C3, based on soil texture and available water capacity (AWC), which had high, moderate, and low yield potential, respectively. Three elevations (downslope (DS), midslope (MS), and upslope (US)) were considered as the second study factor. An unbalanced experimental design was adopted with a factorial analysis of variance for data analysis. Temporal growth analysis showed that soil classes and elevation had significant effects. Generally, better growth was observed in C1 compared to that of C3. DS had a lower yield potential than that of MS and US. In addition, the interactive effect was confirmed, as triticale had poor growth and yield in C3 on the DS, but not on US. Crop physiological parameters also confirmed the differences between soil classes and elevation. Similarly, soil moisture (SM) content in the plow layer measured at different points in time and AWC over the soil profile had a positive association with growth and yield. The results confirmed that spatial differences in AWC and SM can explain spatial variability in growth and yield. The mapping approach combining soil auguring techniques with a digital elevation model could be used to subdivide fields in hummocky landscapes for determining sub-field input intensities to guide precision farming.

The live water storage of the reservoirs is decreasing by the sedimentation, which is affecting the reservoir’s capacity and cause a severe problem for the irrigation system at the downstream side. Floods occur at the downstream by the poor management at upstream due to the heavy rainfall and snow melting. For annual accumulations of sediment load and estimation of the peak flow at Tarbela reservoir near Besham Qila station having area of 170,000 km2 was selected. Estimation of the peak flow and sediment yield at the Tarbela reservoir, SWAT (distributed hydrological model) was used. The expected decrease in reservoir storage capacity was also estimated with the SWAT model. For runoff modelling, calibration was done for three years (2004-2006) and validation was also done for three years (2007-2009). Nash-Sutcliffe Efficiency (NSE) and Standard Error of Estimate existed the statistical indices to evaluate the results. Coefficient of determination (R2) was found as 0.75 for the calibration period and 0.80 for the validation. Whereas, NSE for calibration was observed 0.69 and 0.70 for the validation. Monthly mean sediment yield was about 0.13 BCM estimated at the Tarbela reservoir near Besham Qila.

No abstract