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Precision agriculture has been increasingly practised in recent years. Under precision management, farmland is divided into several management zones to implement different strategies and improve irrigation water productivity and revenues. In this research, six soil properties (silt, sand, soil moisture content, available nitrogen, electrical conductivity and elevation) were selected, and fuzzy c-means clustering was used to delineate management zones. The field was divided into three zones. The differences in the mean of the soil properties among the zones were large and within a zone were small. The coefficient of variation of the properties and yield were also smaller than that before classification. The optimization was carried out by using a genetic algorithm based on the Jensen model. Three objective functions were set as maximum yield, maximum irrigation water productivity (IWP) and maximum revenue, and the weights were kept equal to 1/3. The WHCNS (soil water heat carbon nitrogen simulator) model was used to simulate the maize yield under optimized irrigation schedule for the three management zones and to calculate IWP and revenue. Compared with uniform management, IWP and revenue were increased by 0.6 kg m−3 and $61 ha−1, respectively. The optimized irrigation schedule can be used as a reference for the actual irrigation management. It can increase the IWP and revenue under the premise of achieving the target yield. The results show that the method can guide precision agricultural production and management in large-scale farmland. [ABSTRACT FROM AUTHOR]

The impact of tropical cyclones (TCs) on the CO2 partial pressure at the sea surface (pCO2sea) and air‐sea CO2 flux (FCO2) in the Bay of Bengal (BoB) was quantified based on satellite data and in situ observations between November 2013 and January 2017. The in situ observations were made at the BoB Ocean Acidification mooring buoy. A weak time‐mean net source of 55.78 ± 11.16 mmol CO2 m−2 year−1 at the BoB Ocean Acidification site was estimated during this period. A wide range in increases of pCO2sea (1.0–14.8 μatm) induced by TCs occurred in postmonsoon (October–December), and large decreases of pCO2sea (−14.0 μatm) occurred in premonsoon (March–May). Large vertical differences in the ratio of dissolved inorganic carbon (DIC) to total alkalinity (TA) in the upper layer (ΔDIC/TA) were responsible for increasing pCO2sea in postmonsoon. Relatively small values of ΔDIC/TA were responsible for decreasing pCO2sea in premonsoon. Five TCs (Hudhud, Five, Kyant, Vardah, and Roanu) were considered. Hudhud significantly enhanced CO2 efflux (18.49 ± 3.70 mmol CO2/m2) in oversaturated areas due to the wind effect during the storm and wind‐pump effects after the storm. Vardah insignificantly changed FCO2 (1.22 ± 0.24 mmol CO2/m2) in undersaturated areas because of the counteraction of these two effects. Roanu significantly enhanced CO2 efflux (19.08 ± 3.82 mmol CO2/m2) in highly oversaturated conditions (ΔpCO2 > 20 μatm) since the wind effect greatly exceeded the wind‐pump effects. These five TCs were estimated to account for 55 ± 23% of the annual‐mean CO2 annual efflux, suggesting that TCs have significant impacts on the carbon cycle in the BoB. Plain Language Summary: We examined the impact by five tropical cyclones on the sea surface pCO2 and air‐sea CO2 exchange using Bay of Bengal Ocean Acidification moored buoy measurements over the Bay of Bengal. To our knowledge, this is the first study on the different effects of tropical cyclones and vertical differences in the ratio of dissolved inorganic carbon to total alkalinity in the upper layer on the sea surface pCO2 and local air‐sea CO2 flux. Key Points: TCs tended to increase the pCO2sea in the postmonsoon and to decrease the pCO2sea in the premonsoon in the Bay of BengalLarge vertical differences in the ratio of DIC to TA in the upper layer were responsible for increasing pCO2sea induced by TCsTCs were estimated to account for 55 ± 23% of the annual‐mean CO2 annual efflux [ABSTRACT FROM AUTHOR]

This study investigates the processes of occasional sea surface temperature (SST) fronts and their impacts on chlorophyll-a concentration (chl-a) in the South China Sea (SCS), based on satellite remote sensing and in situ observations in 2009-2013. The SST fronts were detected by an entropy-based edge detection algorithm method from satellite-derived SST images with a 0.011° grid size. Three offshore SST front case studies (S1, S2 and S3) at the peripheries of eddies in the northern SCS were studied. In case S1 in September 2013, two SST fronts were detected with gradient magnitudes (GMs) greater than 0.06°C km-1 in the cyclonic eddy and 0.08°C km-1 in the periphery waters, and the fronts only existed for one and two days, respectively. After three and seven days, the high chl-a was found in the strong SST front waters which were about 51 and 54% higher than the concentration in the surrounding waters. The depth of the maximum chl-a elevated from the subsurface (50 m) to the surface. In case S2 in August 2013, two SST fronts were detected at the periphery of an anti-cyclonic eddy with GM stronger than 0.06°C km-1 and only existed for one day. After two days, the chl-a in the SST front waters was about 40% higher than the levels in the surrounding waters. In case S3 in June 2009, the GM of the eddy-feature SST front was stronger than 0.12°C km-1 and existed for three days, which was generated by tropical cyclone Linfa. The chl-a in the eddy-feature phytoplankton bloom was 6 times higher than in the surrounding waters. The results show that, in general, occasional offshore SST fronts at peripheries of eddies have stronger influence on surface chl-a, comparing to those seasonal coastal and permanent offshore SST fronts, via ‘Wind Pump’ effects. [ABSTRACT FROM AUTHOR]