Silverleaf nightshade (Solanum elaeagnifolium Cav.) has become a highly troublesome weed in irrigated summer crops in Israel. Because herbicide-based options to control this weed are limited, the best way to improve weed control is through a study of its biology, particularly its germination dynamics. The main objective of this study was to determine the impact of temperature on the seed germination dynamics of S. elaeagnifolium and to develop a temperature-based (thermal) prediction model for three S. elaeagnifolium populations growing in different ecosystems in Israel. To this end, a laboratory study was undertaken in which the germination proportion of S. elaeagnifolium seeds was monitored under seven temperature regimes: 2/8, 7/13, 12/18, 17/23, 22/28, 27/33, and 32/38 C (night/day). In addition, the impact of alternating temperature regimes between night and day temperatures (of 0, 2, 4, 6, 8, and 10 C), averaged over 20 and 25 C, was determined. It was found that the three populations shared similar germination characteristics and dynamics. An alternation of ≥6 C between night and day temperatures was needed for optimal germination, with no germination taking place under constant temperatures. In all three populations, the minimal requirement for germination was a 12/18 C (night/day) regime, with the final germination proportion lying between 0.25 and 0.36. The highest final germination proportion of ≥0.8 was observed for the 17/23 C regime in all three populations. Modeling the germination rate as a function of temperature allowed us to determine cardinal temperatures for all three populations taken together, with the values being Tb = 10.8 C (base temperature), To = 23.8 C (optimal temperature), and Tc = 35.9 C (ceiling temperature). These biological parameters allowed accurate (root mean-square error < 0.06%) prediction of S. elaeagnifolium seed germination over the entire temperature range.

Two major properties that determine weed seed germination are soil temperature and moisture content. Incident radiation is the primary variable controlling energy input to the soil system and thereby influences both moisture and temperature profiles. However, many agricultural field sites lack proper instrumentation to measure solar radiation directly. To overcome this shortcoming, an empirical model was developed to estimate total incident solar radiation (beam and diffuse) with hourly time steps. Input parameters for the model are latitude, longitude, and elevation of the field site, along with daily precipitation with daily minimum and maximum air temperatures. Field validation of this model was conducted at a total of 18 sites, where sufficient meteorological data were available for validation, allowing a total of 42 individual yearly comparisons. The model performed well, with an average Pearson correlation of 0.92, modeling index of 0.95, modeling efficiency of 0.80, root mean square error of 111 W m−2, and a mean absolute error of 56 W m−2. These results compare favorably to other developed empirical solar radiation models but with the advantage of predicting hourly solar radiation for the entire year based on limited climatic data and no site-specific calibration requirement. This solar radiation prediction tool can be integrated into dormancy, germination, and growth models to improve microclimate-based simulation of development of weeds and other plants.