Abstract: Wireless sensor networks (WSN) have been widely adopted for monitoring of agro-ecological environment, as they offer a number of advantages over traditional field observation methods. Signal transmission distances and qualities achieved by wireless sensors are highly related to the types of external environments. Attenuation of radio signals varies drastically for wireless sensor networks in different agro-ecological environments with diverse physical forms and structures. To achieve the economic, rational, and efficient goal for WSN deployment, it is essential to identify the effective transmission distance between wireless sensors in typical agro-ecological environments. This paper employed a long attenuation model, known as a Shadowing model, to examine the effect of distance on signal propagation loss with given transmitting power by measuring signal strength at the receiving node in one-hop networking experiments. The network was constructed using 12 nodes with commonly adopted CC2530 and CC2591 as wireless communication modules (both working at 2.4GHz ISM frequency band) and four different landscape settings as typical analysis environments (i.e. lake, grassland, low shrubs, and woodland). To improve the signal sending and receiving capacity, wireless sensor nodes under experiment were equipped with a 5 dB short-stick antenna. The initial test was conducted to determine the minimum sensitivity of sensor nodes to be -97 dBm. By setting a series of distances between data sending and receiving sensor nodes, the corresponding received signal strength indication (RSSI) was recorded. A Matlab-based nonlinear regression model was built with the recorded RSSI data to analyze the relationship between RSSI and transmission distance for each of the four agro-ecological environments. The resulting coefficients of determination for the regression models indicated a strong relationship between RSSI and transmission distance, as they complied with the Shadowing model with a degree of fitting between 0.9232-0.9556. According to the fitted curves in the regression analyses, a transmission path loss index was calculated to represent such interferences as attenuation, reflection, and multi-path phenomenon on wireless signals due to a given environmental morphology and structure. With the signal sending power being kept constant, it was found that lakes had the lowest transmission path loss index value (2.1), which was followed by grassland (2.4), low shrubs (2.6), and finally woodland (3.1). Based on the fitted regression models and adopting the minimum sensitivity of sensors as the threshold, the calculated theoretical maximum transmission distance for the deployment of the given sensor nodes was 663.3m, 419.3m, 155.2m, and 79.5m for lake, grassland, low shrubs, and woodland, respectively. Effective transmission distances were also computed from the theoretical ones by a 25% deduction, resulting in 495m, 330m, 150m and 65m for the above four ecological settings, respectively. The experimental procedure and the transmission path loss index estimated by the fitted regression models in this paper can provide useful reference for practical environmental monitoring network construction and sensor node deployment when facing diverse environmental morphologies. Potential further work will include investigating effects of node height variation on transmission distances in different agro-ecological environments and experimenting and comparing the results of this study with sensor nodes equipped with other wireless modules.