Adaptive management framework for growth response analysis of tomato in controlled environment plant production systems

High demands for quality agricultural products require practicing modern techniques of resource management in controlled environment plant production systems (CEPPS). The cost of growing inside closed-field is generally higher than producing in open-field; therefore a comprehensive understanding and analysis of environment responses (ER), plant requirements and growth responses (GR) are necessary to embrace uncertainties in such environments. An adaptive management framework (AMF) was developed and used in this study for defining and determining foundation classes (climate control parameters) and objects (tomato crop at different growth stages and light condition) in a bioproduction system like CEPPS. The flexible architecture of the framework with a self-tuning configuration database enables it to work with different culture classes and objects within which many specific scenarios may be modeled and analyzed. This design proposes a systematic approach for the immense environmental data analyzing tasks with the overall objective of providing knowledge-based information for achieving optimal climate condition. The framework adaptive database was built according to peer-reviewed published works that define probability of successful production of tomato (Lycopersicon Esculentum) as individual growth response functions (GRF) for air temperature and relative humidity (RH) at five growth stages (GS) and under three light conditions (night, sun, cloud). Background knowledge from scientific literatures was used with a numerical method approach in developing response functions for vapor pressure deficit (VPD). The framework was used in two separate case studies: (i) open-field, with total of 126 data collection days (from June to December, 2013) and (ii) closed-field (including three environments, denoted by A: OFE, B: PFCE, and C: PPCE) with 11 days of data collection. The output results were generated for one-day and multi-days based analysis, including preliminary statistics and inferences, dynamic visualization plots, environment responses (ER) to optimal parameter x (where x represents temperature, RH or VPD), growth responses (GR) analysis, optimization and reference selection, comparison factors, maximum guaranteed and actual growth response, performance curves, adaptability factors, light-condition based analysis and prediction models. A new term, digital growth response map, was introduced and demonstrated, providing time-specific information on environment performance. For each case study, environment responses, ER(x), at three references (GR=0, GR=0.55 and GR=1) were calculated for all growth stages. Factorial design was used to determine variation in data due to different months and stages. Results of ER analysis indicated possible savings of energy up to 62% at growth stage=1, 17% at stage=2 and 30% at stage=3 to 5, in providing ideal climate condition for closed-field production of tomato. In addition, analysis of growth responses, showed that averaged probability of successful production, associated with temperature, RH and VPD (denoted by GR(T), GR(RH) and GR(VPD)) were 0.71, 0.69 and 0.75 respectively. It was observed that in each month, minimum values of GR(T), regardless of growth stage, occurred between 11:00am to 7:00pm. While this trend was significant for GR(RH) at stage=1, the minimum values of GR(RH) for stage=2 and stage=3-to-5 appeared from 2:00am to 6:00am. The results light-condition-based analysis showed that maximum temperature and VPD values occur at sun condition, with peak values between 11:00am to 4:00pm, when RH is at minimum, and the lowest VPD values belong to night hours. It was found that the averaged GR(x) based on light conditions depends on the growth stage. For example, in the openfield case study, at stage=1, averaged GR(T) in the entire 6 months was found to be the highest at night times compared with sun and cloud light conditions, while at stage=2 to 5, sun condition had the highest average value for GR(T). The result of the second case study indicated significant difference between three environments in the peak-hours of energy requirement. It was observed that at temperature between 20°C to 30°C, RH between 80% and 100%, and VPD between 0.1kPa to 1.2kPa, all three environments are almost equally providing same growth condition for tomato, however, as temperature starts rising above 30°C, differences in the environments starts growing. The proposed approach can be used to evaluate any environment for greenhouse production, and to provide required information for management decisions such as scheduling efficiencies, site-selection, cost evaluation, energy prediction and risk assessments associated with each task.

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