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Crop models need accurate simulation of leaf canopy development. The thermal interval for leaf tip appearance (phyllochron) is critical for predicting the duration of vegetative development. The phyllochron in maize is shorter in temperate than in tropical and subtropical environments. As existing data has been evaluated in a narrow range of environments, the underlying mechanisms that affect phyllochron have not been adequately examined. The objectives of this study were to quantify the response of phyllochron to environmental variables and determine its stability across maize cultivars, to aid modelers in developing tools which accurately predict phenology. Maize was grown in field experiments at Wageningen, The Netherlands, Temple, Texas, USA, and three sites in Mexico, and in controlled environments at Wageningen. The experiment at Temple included grain sorghum and shading treatments to alter irradiance of the crop. Detailed data on leaf production and environmental conditions were collected. These data were combined with published data from field studies. Maize phyllochron acclimated to temperature and increased as mean daily temperature before tassel initiation increased from 12.5 to 25.

from emergence to tassel initiation, well before LAI tot is reached (Kiniry, 1991). Tassel initiation occurs at ap-Leaf area development at regular time intervals during the growing proximately the 7-to-12-visible-leaves stage, depending season is estimated by many crop growth models and is needed for competition studies. In plant breeding programs, observation is gener-on the relative maturities of the varieties and the photoally restricted to a single leaf area estimate or assessment of the plant periods to which they are exposed (G.O. Edmeades, type. Two procedures that build on earlier studies are presented to personal communication, 1998). However, all menestimate total plant leaf area of maize (Zea mays L.). Leaf area tioned studies were conducted under nonlimiting development of six tropical maize cultivars grown in 1995 and 1996 in growth conditions. Furthermore, Birch et al. (1998), several tropical environments in Mexico (both favorable and moistureworking with five cultivars, observed genetic variation and N-limited) was observed and analyzed. First, the validity of a in parameter values. The question remains, therefore, bell-shaped curve describing the area of individual leaves as a funcwhether relationships between LAI tot and the parameter tion of leaf number was investigated. When individual cultivarset describing this bell-shaped curve are valid for wider environment combinations were normalized for area of the largest sets of germplasm and growing conditions. leaf and for total leaf number, one parameter set described all combinations. It remained difficult, however, to estimate these parameters Mathematical relationships between length, width, in advance, which limits predictive applications in crop growth models. and area of maize leaf blades can serve as a basis for Analytical application after flowering, when parameter values can be direct leaf area estimation. Montgomery (1911) found determined, is possible. Second, a method was developed to directly that the area of a maize leaf blade can be estimated as estimate total leaf area when total leaf number and area of the largest its length multiplied by its maximum width multiplied leaf are known. The method makes use of the facts that the area of by 0.75. In a modification of this method, Lal and Subba the largest leaf relative to total plant leaf area is constant and that Rao (1951) proposed a logarithmic transformation of this constant is linearly related to total leaf number. This study has leaf length and width. Total plant leaf area can therefore shown that approaches previously presented by others are applicable be obtained by measuring the lengths and widths of all in modified form over a wide range of environmental conditions.

No abstract

The global population is increasing rapidly, together with the demand for healthy fresh food. The greenhouse industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) has reached breakthroughs in several areas, however, not yet in horticulture. An international competition on “autonomous greenhouses” aimed to combine horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams, consisting of scientists, professionals, and students with different backgrounds in horticulture and AI, participated in a greenhouse growing experiment. Each team had a 96 m2 modern greenhouse compartment to grow a cucumber crop remotely during a 4-month-period. Each compartment was equipped with standard actuators (heating, ventilation, screening, lighting, fogging, CO2 supply, water and nutrient supply). Control setpoints were remotely determined by teams using their own AI algorithms. Actuators were operated by a process computer. Different sensors continuously collected measurements. Setpoints and measurements were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a manually operated reference. Detailed results on cucumber yield, resource use, and net profit obtained by teams are explained in this paper. We can conclude that in general AI performed well in controlling a greenhouse. One team outperformed the manually-grown reference.