Possibilities for soilless cultivation in cut chrysanthemum: Effect of irrigation frequencies and spacing schedules

Three levels of irrigation frequencies, provided by root misting, combined with three plant densities and two spacing treatments were tested to evaluate the optimum conditions during the first crop stages of chrysanthemum in a soilless cultivation system (aeroponics) in an experiment conducted in autumn. The optimum misting frequency was 3¿2’ times ¿ min h-1. A higher frequency (12¿1’) had no additional effect, whereas the lowest frequency (1¿6’) had a negative effect on total shoot dry mass (TDMs). The highest plant densities (172 and 344 plants m-2) could be used until week 2 with hardly any negative effect on TDMs, and resulted in higher light interception and higher total shoot dry mass per m2. During the period between week 2 and 4 after planting, a higher density (172 compared to 86 plants m-2) had a strong negative impact on the TDMs, while a further increase to 344 plants m-2 had only a minor effect. When spacing (week 2) from 344 to 172 plants m-2, TDMs at week 4 was not negatively affected by the high starting density, though spacing from 172 to 86 plants m-2 resulted in a 13% reduced TDMs, as compared to plants grown at 86 plants m-2 continuously. It is concluded that the irrigation frequency until week 4 after planting under these light conditions, should be three times per hour. Furthermore, very high plant densities (e.g., 344 plants m-2) are feasible until week 2 with hardly any negative effects on plant growth, while spacing schemes give several possibilities for a smaller reduction of the TDMs, than that expected by the higher initial densities

Effect of Relative Air Himidity on the Stomatal Functionality in Fully Developed Leaves

Several studies have shown that stomata developed under long-term high relative air humidity (RH =85%) are malfunctional, resulting in a poor control of water loss. Yet, little is known about the dynamics of stomatal adaptation to moderate RH, and the possibilities to improve or reverse the destabilized stomatal responsiveness. In this study, a reciprocal transfer experiment was conducted in climate chambers using Rosa hybrida ‘Prophyta’, grown at moderate RH (60%) or at high RH (90%). The adaptation of fully developed leaves to the new RH environment was assessed at day 0, 4, 8 and 12 after plant transfer by measuring the transpiration rate in detached leaves. Stomata fully developed at high RH had a lower closing capacity in response to a decrease in leaf Relative Water Content (RWC) (i.e. water loss was considerably high at RWC below 20%, whereas in moderate RH stomata the water loss almost ceased at 57% RWC). Furthermore, stomata developed at high RH did not become functional after 12 days of cultivation at moderate RH. Similarly, stomata developed at moderate RH and transferred to high RH for a 12 day period did not loose their ability to close in response to desiccation. This indicates that stomatal functionality is determined during leaf development, while after this period stomata have a limited capacity to adapt to new RH environment. It is concluded that stomata from fully developed rose leaves conserve their behaviour independently of the post-development humidity leve

Genotypic variation of cut chrysanthemum response to high CO2 concentration: Growth, time to flowering and visual quality

In this study sixteen cut chrysanthemum cultivars were used to evaluate the effects of high CO2 concentration (1500 µmol mol-1) on growth, time to flowering and visual quality as compared to the concentration used in commercial greenhouses (600 µmol mol-1). CO2 enrichment increased light use efficiency (11-41%) and total plant dry mass (TDM) (5-40%) in a cultivar dependent manner. This TDM increase was a result of: (i) higher relative growth rate during the long day period (i.e., 0 to 2 weeks; LD); and (ii) higher absolute growth rate both during the period between 2 to 6 weeks (SD1), and 6 weeks to final harvest (SD2). Cultivar differences in TDM at flowering between the two CO2 concentrations could be explained by differences in growth rate during the LD and SD2 periods. Furthermore, growing at high CO2 regime enhanced the number of flowers and flower buds per plant (NoF, 4-48%). Interestingly, the cultivars that showed the highest percentage of TDM increase, with CO2 enrichment, were not the ones that had the highest increase in the percentage of NoF. In contrast, high CO2 concentration had only a minor or no effect on the number of internodes on the main stem and on the reaction time in all the cultivars examined. From this research it is concluded that there is a large variation in the response of cut chrysanthemum cultivars to CO2 enrichment, in terms of TDM and NoF, which gives possibilities for breeding

Multiscale Soil Investigations: Physical Concepts And Mathematical Techniques

Soil variability has often been considered to be composed of “functional” (explained) variations plus random fl uctuations or noise. However, the distinction between these two components is scale dependent because increasing the scale of observation almost always reveals structure in the noise (Burrough, 1983). Soils can be seen as the result of spatial variation operating over several scales, indicating that factors infl uencing spatial variability differ with scale. Th is observation points to variability as a key soil attribute that should be studied

Nationwide outbreak of STEC O157 infection in the Netherlands, December 2008-January 2009: continuous risk of consuming raw beef products.

The Netherlands experienced a nationwide outbreak of Shiga toxin-producing Escherichia coli (STEC) O157 with onset of symptoms from the end of December 2008 until the end of January 2009. A total of 20 laboratory-confirmed cases were linked to the outbreak strain, serotype O157: H-, stx1, stx2, eae and e-hly positive. The investigation into the source of this outbreak is still ongoing, but evidence so far suggests that infection occurred as a result of consuming contaminated raw meat (steak tartare)

Continuous Light as a way to increase Greenhouse Tomato Production

Tomato plants need six hours of darkness per day for optimal growth; therefore, photosynthesis does not take place for 25% of the day. If tomatoes could be grown under continuous light, a substantial increase in production is expected. In practice, however, continuous light-grown tomato plants develop a potentially lethal mottled chlorosis. Such continuous-light-induced injury is only poorly understood so far. Recently, we proposed a number of hypotheses that aim to explain the continuous-light-induced injury, and rediscovered that wild-tomato species were reported as continuous-light-tolerant. Here, we (i) present a simulation study which shows that if an ideal continuous-light-tolerant tomato genotype is used and no crop adaptations to continuous light are assumed, greenhouse tomato production could be 26% higher when using supplementary lighting for 24 h day-1 in comparison with using supplementary lighting only for 18 h day-1 during day time, and (ii) discuss expected changes in greenhouse energy budgets and alterations in crop physiological responses that might arise from cultivating tomatoes under continuous light

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0–30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms