Capturing spatial variability of factors affecting the water allocation plans-a geo-informatics approach for large irrigation schemes.

The livelihoods of poor people living in rural areas of Indus Basin Irrigation System (IBIS) of Pakistan depend largely on irrigated agriculture. Water duties in IBIS are mainly calculated based on crop-specific evapotranspiration. Recent studies show that ignoring the spatial variability of factors affecting the crop water requirements can affect the crop production. The objective of the current study is thus to identify the factors which can affect the water duties in IBIS, map these factors by GIS, and then develop the irrigation response units (IRUs), an area representing the unique combinations of factors affecting the gross irrigation requirements (GIR). The Lower Chenab Canal (LCC) irrigation scheme, the largest irrigation scheme of the IBIS, is selected as a case. Groundwater quality, groundwater levels, soil salinity, soil texture, and crop types are identified as the main factors for IRUs. GIS along with gamma design software GS + was used to delineate the IRUs in the large irrigation scheme. This resulted in a total of 84 IRUs in the large irrigation scheme based on similar biophysical factors. This study provided the empathy of suitable tactics to increase water management and productivity in LCC. It will be conceivable to investigate a whole irrigation canal command in parts (considering the field-level variations) and to give definite tactics for management.

Modelling spatio-temporal patterns of soil carbon and greenhouse gas emissions in grazing lands: Current status and prospects.

The sustainability of grazing lands lies in the nexus of human consumption behavior, livestock productivity, and environmental footprint. Due to fast growing global food demands, many grazing lands have suffered from overgrazing, leading to soil degradation, air and water pollution, and biodiversity losses. Multidisciplinary efforts are required to understand how these lands can be better assessed and managed to attain predictable outcomes of optimal benefit to society. This paper synthesizes our understanding based on previous work done on modelling the influences of grazing of soil carbon (SC) and greenhouse gas emissions to identify current knowledge gaps and research priorities. We revisit three widely-used process-based models: DeNitrification DeComposition (DNDC), DayCent, and the Pasture Simulation model (PaSim) and two watershed models: The Soil & Water Assessment Tool (SWAT) and Variable Infiltration Capacity Model (VIC), which are widely used to simulate C, nutrient and water cycles. We review their structures and ability as process-based models in representing key feedbacks among grazing management, SOM decomposition and hydrological processes in grazing lands. Then we review some significant advances in the use of models combining biogeochemical and hydrological processes. Finally, we examine challenges of incorporating spatial heterogeneity and temporal variability into modelling C and nutrient cycling in grazing lands and discuss their weakness and strengths. We also highlight key research direction for improving the knowledge base and code structure in modelling C and nutrient cycling in grazing lands, which are essential to conserve grazing lands and maintain their ecosystem goods and services.

Optimizing farmyard manure and cattle slurry applications for intensively managed grasslands based on UK-DNDC model simulations.

Fertilizer applications can enhance soil fertility, pasture growth and thereby increase production. Nitrogen fertilizer has, however, been identified as a significant source of nitrous oxide (N2O) emissions from agriculture if not used correctly and can thereby increase the environmental damage costs associated with agricultural production. The optimum use of organic fertilizers requires an improved understanding of nutrient cycles and their controls. Against this context, the objective of this research was to evaluate the scope for reducing N2O emissions from grassland using a number of manure management practices including more frequent applications of smaller doses and different methods of application. We used a modified UK-DNDC model and N2O emissions from grasslands at Pwllpeiran (PW), UK during the calibration period in autumn, were 1.35 kg N/ha/y (cattle slurry) and 0.95 kg N/ha/y (farmyard manure), and 2.31 kg N/ha/y (cattle slurry) and 1.08 kg N/ha/y (farmyard manure) during validation period in spring, compared to 1.43 kg N/ha/y (cattle slurry) and 0.29 kg N/ha/y (farmyard manure) during spring at North Wyke (NW), UK. The modelling results suggested that the time period between fertilizing and sampling (TPFA), rainfall and the daily average air temperature are key factors for N2O emissions. Also, the emission factor (EF) varies spatio-temporally (0-2%) compared to uniform 1% EF assumption of IPCC. Predicted N2O emissions were positively and linearly (R2 ≈ 1) related with N loadings under all scenarios. During the scenario analysis, the use of high frequency, low dose fertilizer applications compared to a single one off application was predicted to reduce N2O peak fluxes and overall emissions for cattle slurry during the autumn and spring seasons at the PW and NW experimental sites by 17% and 15%, respectively. These results demonstrated that an optimized application regime using outputs from the modelling approach is a promising tool for supporting environmentally-friendly precision agriculture.

Modelling of nitrification inhibitor and its effects on emissions of nitrous oxide (N2O) in the UK.

Global food demand requires increased uses of fertilizers, leading to nitrous oxide (N2O) and nitrate leaching due to overuse of fertilizers and poor timing between fertilizer application and plant growth. Using nitrification inhibitors (NIs) can reduce the N2O emissions but the effectiveness of NIs strongly depend on environmental conditions, and their benefits have been limited due to less than optimal nitrogen rates, timing, quantity, and placement of NIs. Process-based modelling can be helpful in improving the understanding of nitrogen fertilizer with NIs and their effects in different environmental conditions and agricultural practices. But few studies of modelling NIs with application to agricultural soils have been performed. In this paper, we developed a sophisticated biogeochemical reaction process of NIs applied to agricultural soils, which account for the factions of NIs with fertilizer by combining the application rate, soil moisture, and temperature within the DeNitrification DeComposition (DNDC) framework. This model was tested against the data from two agricultural farms in Preston Wynne and Newark in the UK. The results agreed well with the measured data and captured the measured soil moistures and N2O emissions. In Newark, the average Mean Absolute Error of all blocks is 8.78 and 5.45 for ammonium nitrate or urea respectively while in Preston Wynne, 3.48 and 3.14. The results also showed that the warming climate can greatly reduce the efficiency of nitrification inhibitors, which will further amplify the greenhouse gas impacts. The modified DNDC model of nitrification inhibitor modules can reliably simulate the inhibitory effect of NIs on N2O emissions and evaluate the efficiency of NIs. This enables end-users to optimize the amount of NIs used according to the time and climate conditions of fertilizer application for increasing crop yield and reducing N2O emissions and provides a useful tool for estimating the efficiency of NIs in agricultural management.