An Experimental Comparison of IoT-Based and Traditional Irrigation Scheduling on a Flood-Irrigated Subtropical Lemon Farm.

Over recent years, the demand for supplies of freshwater is escalating with the increasing food demand of a fast-growing population. The agriculture sector of Pakistan contributes to 26% of its GDP and employs 43% of the entire labor force. However, the currently used traditional farming methods such as flood irrigation and rotating water allocation system (Warabandi) results in excess and untimely water usage, as well as low crop yield. Internet of things (IoT) solutions based on real-time farm sensor data and intelligent decision support systems have led to many smart farming solutions, thus improving water utilization. The objective of this study was to compare and optimize water usage in a 2-acre lemon farm test site in Gadap, Karachi, for a 9-month duration, by deploying an indigenously developed IoT device and an agriculture-based decision support system (DSS). The sensor data are wirelessly collected over the cloud and a mobile application, as well as a web-based information visualization, and a DSS system makes irrigation recommendations. The DSS system is based on weather data (temperature and humidity), real time in situ sensor data from the IoT device deployed in the farm, and crop data (Kc and crop type). These data are supplied to the Penman-Monteith and crop coefficient model to make recommendations for irrigation schedules in the test site. The results show impressive water savings (~50%) combined with increased yield (35%) when compared with water usage and crop yields in a neighboring 2-acre lemon farm where traditional irrigation scheduling was employed and where harsh conditions sometimes resulted in temperatures in excess of 50 °C.

Predicting Crop Evapotranspiration by Integrating Ground and Remote Sensors with Air Temperature Forecasts.

Water use efficiency in agriculture can be improved by implementing advisory systems that support on-farm irrigation scheduling, with reliable forecasts of the actual crop water requirements, where crop evapotranspiration (ETc) is the main component. The development of such advisory systems is highly dependent upon the availability of timely updated crop canopy parameters and weather forecasts several days in advance, at low operational costs. This study presents a methodology for forecasting ETc, based on crop parameters retrieved from multispectral images, data from ground weather sensors, and air temperature forecasts. Crop multispectral images are freely provided by recent satellite missions, with high spatial and temporal resolutions. Meteorological services broadcast air temperature forecasts with lead times of several days, at no subscription costs, and with high accuracy. The performance of the proposed methodology was applied at 18 sites of the Campania region in Italy, by exploiting the data of intensive field campaigns in the years 2014-2015. ETc measurements were forecast with a median bias of 0.2 mm, and a median root mean square error (RMSE) of 0.75 mm at the first day of forecast. At the 5th day of accumulated forecast, the median bias and RMSE become 1 mm and 2.75 mm, respectively. The forecast performances were proved to be as accurate and as precise as those provided with a complete set of forecasted weather variables.

Towards physiologically meaningful water-use efficiency estimates from eddy covariance data.

Intrinsic water-use efficiency (iWUE) characterizes the physiological control on the simultaneous exchange of water and carbon dioxide in terrestrial ecosystems. Knowledge of iWUE is commonly gained from leaf-level gas exchange measurements, which are inevitably restricted in their spatial and temporal coverage. Flux measurements based on the eddy covariance (EC) technique can overcome these limitations, as they provide continuous and long-term records of carbon and water fluxes at the ecosystem scale. However, vegetation gas exchange parameters derived from EC data are subject to scale-dependent and method-specific uncertainties that compromise their ecophysiological interpretation as well as their comparability among ecosystems and across spatial scales. Here, we use estimates of canopy conductance and gross primary productivity (GPP) derived from EC data to calculate a measure of iWUE (G1 , "stomatal slope") at the ecosystem level at six sites comprising tropical, Mediterranean, temperate, and boreal forests. We assess the following six mechanisms potentially causing discrepancies between leaf and ecosystem-level estimates of G1 : (i) non-transpirational water fluxes; (ii) aerodynamic conductance; (iii) meteorological deviations between measurement height and canopy surface; (iv) energy balance non-closure; (v) uncertainties in net ecosystem exchange partitioning; and (vi) physiological within-canopy gradients. Our results demonstrate that an unclosed energy balance caused the largest uncertainties, in particular if it was associated with erroneous latent heat flux estimates. The effect of aerodynamic conductance on G1 was sufficiently captured with a simple representation. G1 was found to be less sensitive to meteorological deviations between canopy surface and measurement height and, given that data are appropriately filtered, to non-transpirational water fluxes. Uncertainties in the derived GPP and physiological within-canopy gradients and their implications for parameter estimates at leaf and ecosystem level are discussed. Our results highlight the importance of adequately considering the sources of uncertainty outlined here when EC-derived water-use efficiency is interpreted in an ecophysiological context.

[Estimation of evapotranspiration and crop coefficient in Dajiuhu peatland of Shennongjia based on FAO56 Penman-Monteith]. / 基于FAO56 Penman-Monteithå ¬å¼ä¼°ç®—ç¥žå†œæž¶å¤§ä¹æ¹–æ³¥ç‚­æ¹¿åœ°è’¸æ•£åŠä½œç‰©ç³»æ•°.

We collected evapotranspiration data of Dajiuhu peatland in Shennongjia from 2016 to 2017 with eddy covariance method and estimated the value of crop coefficient (Kc) using FAO56 Penman-Monteith equation and the linear relationship between actual evapotranspiration (ETa) and referenced evapotranspiration (ET0). We analyzed the characteristics of referenced evapotranspiration and its main influencing factors and calculated the crop coefficient of the wetland dominated by Sphagnum. The results showed that the daily averaged ETa were 1.63 and 1.38 mm·d-1 in 2016 and 2017, the daily averaged ET0 were 1.61 and 1.23 mm·d-1 in 2016 and 2017. Environmental factors influencing ET0 included net radiation, air temperature, vapor pressure deficit, wind speed, and relative humidity. The Kc values for the growing seasons of 2016, 2017, and 2016-2017 were 0.95 (R2 of linear regression between ETa and ET0 was 0.96), 1.03 (R2=0.95), and 0.98 (R2=0.95). The Kc values in 2016, 2017, and 2016-2017 were 0.92 (R2=0.94), 0.95 (R2=0.89), and 0.93 (R2=0.92). Kc was effective in the range of 0.92-1.03 for the wetland dominated by Sphagnum. The identified parameters could be widely used in studies on climate change, ecosystem services, and water management in peatlands.

[MODIS-driven estimation of regional evapotranspiration in Karst area of Southwest China based on the Penman-Monteith-Leuning algorithm.] / 基于Penman-Monteith-Leuning遥感模型的西南喀斯特区域蒸散发估算.

Karst area in southwestern China is characterized with complex topography, low soil water capacity, and fragile ecosystem. Accurate estimation of regional evapotranspiration is essential for ecological restoration and water resources management in southwestern China. Based on observed evapotranspiration and meteorological data, this study aimed to estimate spatial upscale evapotranspiration using the MOD15A2 LAI and Penman-Monteith-Leuning (PML) model, within which the stomatal conductance and soil wetness index were optimized by the least-square method. The results showed that the modeled ET well fitted with the observations, with the determination coefficient, Nash efficiency coefficient and RMSE being 0.85, 0.75 and 1.56 mm·d-1, respectively. The ET exhibited clear seasonality and reached to its maximum in summer, coinciding with vegetation phenology. The annual ET ranged from 534 to 1035 mm·a-1, with strong spatial heterogeneity which highly related to the precipitation. Evapotranspiration may be affected by precipitation as well as land use types.