greenhouse horticulture in the 21st century; can we stay competitive?

Proceedings symposium GREENHOUSE 2010: Environmentally Sound Greenhouse Production for People: S03.060, (N. Castilla, O. van Kooten, S. Sase and J. Meneses eds.) 28th International Horticultural Congress, Lisbon, August 22-27, 2010

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Greenhouse Horticulture in the 21st Century; can we stay competitive? Olaf van Kooten Horticultural Supply Chains Wageningen University P.O. Box 630, Droevendaalsesteeg 1 6700 AP Wageningen The Netherlands [email protected] Keywords: Market, quality, consumer, innovation, biological variation, production-

consumption-synchronization Abstract

Greenhouse horticulture is an intensive production system with a relatively low resource input per unit of output. It is an essential means of production for large urban conglomerates that are abounding in the present century, while water and chemical resources are dwindling. It is however essential to link these production systems to a consortium of traders, logistic providers and retailers in order to obtain a competitive edge by delivering according to the consumers expectations. It will be shown that competition on price, with a concomitant decrease in quality, results in a diminishing market and a viscous circle of declining price and quality. While a strong emphasis on quality will enhance the market and prices will rise as long as a clear and discernable quality of the final product, as it reaches the consumer, is maintained. By doing longitudinal studies of quality development in batches of products it becomes possible to combine deterministic with stochastic models capable of predicting quality evolution throughout the entire production and supply chain. Several examples will be given of this technique and it will be shown that vertical transparency in the supply chain can augment the profit for all participants, while keeping the entire chain competitive in a fast evolving market. By combining predictive quality information with the proper logistic technology, what we call ‘Quality Controlled Logistics’, it becomes possible to optimize the quality of the separate batches by positioning them in the shelves for the consumers at the moment of optimal maturity. What is necessary are new non-destructive measuring techniques that can determine batch quality characteristics at the period of harvest, or even before that, combined with physiology based models to predict quality evolution from harvest time on. Several models have been developed up till now and examples of their application will be given. INTRODUCTION

One of the major effects of world trade development in the last decades is the rise in retail power. Retail conglomerates like WalMart, Carrefour, Tesco and Ahold have made optimal use of the diminished trade barriers (GATT) and enlarged their businesses to multi billion dollar companies that operate as true international enterprises. They have invested heavily in their strategies and in developing their procurement staff and have created an oligopoly in many countries around the globe. This has created a market driven by cost price. If you can deliver reliably year round according to specifications at a cost price lower than the competition, you are allowed to supply as a producer or a wholesaler (Porter 1998). The solution most producers or wholesalers are looking for is becoming


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big enough economically to gain enough clout for a balanced negotiation position with the retail. However, as WalMart is capable of buying the entire cherry production of Chile in one go, it is doubtful whether any producer group or wholesaler can become big enough to balance that power. As long as producers and wholesalers adhere to the ‘big is beautiful’ strategy they will remain within the cost price market with subsequent small to negative profit margins. A side effect of this is that everyone tries to get away with the lowest possible quality that is still acceptable for the retail. As cost price reduction and volume enlargement both tend to reduce quality of the final products. With electronics and other hardware this is not a problem as they can be reliably produced to function for the period they are expected to function (the average life time of a lap-top is about 2 years). However, with horticultural produce this is different. It results in a large variation in quality over time. Consequently the consumer has a low expectation of quality with the subsequent response in the buying frequency of the consumer (Schepers and Van Kooten, 2006). From research on the flower market in the U.S (Reid 2002) it is evident that the cost price strategy not only diminishes quality it also diminishes consumption and in the long run completely destroys the market. While a recent example of a Dutch wholesaler in ornamentals delivering flowers with a 7 days vase life guarantee to the UK retail, shows that quality can develop a market and finally even result in a doubling of the national consumption of cut-flowers (Kooten and Kuiper, 2009). If we want to comply to the strategy in horticulture of ‘optimal quality for consumption at all times’ we will need to invest in quality prediction models and combine consumer levels of quality attributes with availability predictions into what we call “Quality Controlled Logistics” (Vorst et al., 2007). For this we need to:

a. know the variation in quality within batches and how that will evolve through the chain

b. tune the logistics with the quality development c. synchronize production with consumption

And this Herculean job needs to be done in a cost effective and sustainable way in order to stay competitive!

BATCH QUALITY EVOLUTION MODELS A full description of such a model is given in Schouten et al. (2007). What follows is a short explanation of the rationale behind such models. In fig.1 we see the development of a product property, which is related to a quality attribute, over time (at a certain fixed temperature, where the temperature dependence is according to Arrhenius). We assume in the beginning the product is not yet acceptable for consumption and it is probably harvested somewhere in this period, as the retail will reject too mature products. At a certain time after harvest the product reaches a condition that is acceptable for consumption. The product, especially fruits and fruit like vegetables such as tomatoes, can be called ripe then. Some time later the product becomes unacceptable for consumption and can be called over-ripe then. When a batch of products with a singular genetic composition, i.e. one cultivar, is harvested at one location within a single day we can say that the batch consists of one genotype and one phenotype. Consequently the interior biochemical pathways within the separate products of the batch are closely comparable, resulting in one rate constant, one activation energy and one final asymptote for the entire batch. However due to minor variations in microclimate as well as internal transport fluctuations there is a variation in maturity between the different products in the


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batch. This difference is depicted by a time difference t, which is specific for each product. What we discovered in all our experiments, was that this t is normally distributed in every batch (fig. 2) see Tijskens et al. (2005). These mechanistic models can be derived by doing longitudinal analyses on individual products in the batch. Once the models have been determined one can do cross sectional analyses at certain points in time on similar batches. As can be seen in fig. 3 when the measurement is done at the moment of harvest as in this hypothetical case we will get a property distribution that is heavily skewed to the left. If the measurement is done halfway the maturation process the distribution of the property will be normal. And finally when the property reaches the unacceptable stage the distribution will be skewed again, but this time to the right (see insets fig 3). A clear example of the development of a product property over time can be seen in fig. 4 where the colour of Gala apples has been followed on the sunny side of the trees in a single orchard. The apples where measured from the moment the petals had fallen and the fruit bodies started to swell. It is evident that the colour distribution of the apples evolves as predicted in fig. 3. From this it becomes possible to devise stochastic models based on distributions of properties in batches of products (Schouten et al. 2007; Hertog et al. 2007). With these models it becomes possible to predict the exact behaviour of the products in the batch throughout the supply chain if all conditions are known in advance. By combining this knowledge to decision making in the logistic process it becomes possible to position the batches in the retail shelves at their optimum quality level. We call this Quality Controlled Logistics (QCL), see Vorst et al. (2007). WHAT DO WE NEED?

When we can implement QCL it will create the basis for a constant positive experience of consumers for certain products. This allows the horticultural industry to start branding these products and create trust among consumers that they will get what they expect when they buy a product with a certain brand. This has hardly been done successfully before, except in some ready-to-eat strategies (Schepers and Kooten van, 2006) of mangos at the retail company called Albert Heijn and in the case of the flower vase life guarantee by Dutch wholesaler called Intergreen BV. But there are other aspects of quality that need to be taken into account, e.g. just in time delivery (JIT), price, availability, sustainability of the entire Value Chain, etc. With the growth of the earth’s population and the explosive rise in urbanisation, we are faced with tremendous problems in the near future. In order to solve these problems we will need totally different solutions from the present day attempts to improve our production and logistics. As space, clean water, nutrients and clean air are becoming less abundant, while traffic congestions and cities with more than one million inhabitants become more abundant, production in open fields and in greenhouses becomes problematic. If the products could be produced very close to the retail outlet and synchronised to the expected consumer demand in a sustainable and reliable way, many problems can be solved at the same time. PLANTLAB®

In the Netherlands a company called PlantLab has joined forces with Philips and Imtech to create economically viable production units for vegetables and ornamentals (www.plantlab.nl). As can be seen in fig. 5 their units allow for controlling nearly every factor necessary for optimal plant growth. Their results up till now with lettuce (Sala Nova of Rijk Zwaan) and Fitonia, a small pot plant, show that it is possible to create top quality products at a price comparable to present day production in modern green houses


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in the Netherlands. However the possibilities evolving from such a means of production are staggering. As can be seen in fig. 6 such a production unit could be located below a shopping mall in a city. It is estimated for each person to obtain 200 g of fresh vegetables each day 1 m2 will be necessary. As the production can be fully mechanised and done in many levels the total amount of space necessary to feed large amounts of people is very restricted. The logistics become easily controllable and as production times are more or less fixed it becomes possible to synchronise production with the expected consumer demand. Also the water use efficiency (Kooten et al. 2006) goes to near 100% and disease pressure is down to nil as was found up till now with zero use of pesticides during the last 4 years. It becomes possible to create any kind of product with the desired characteristics any place in the world and at any time of the year. For plant breeding it allows for true genotype selection as the phenotype is fixed by applying certain recipes for growth management. Therefore such a system could minimise resource input while maximizing output and quality.

Still a host of questions remain and research is absolutely necessary to improve the system. In Den Bosch at the Applied Agricultural University a study unit has been devised to develop the system, while practical units are operating momentarily at certain growers in the Netherlands. PlantLab is open to any research that can help solve fundamental questions about growth in closed controlled environments. The sophisticated research units that are used by PlantLab for their multilayer development are also available for scientists. For that purpose they developed a complete research centre based on reefers, to be build in The Netherlands by Imtech and shipped all over the world (fig. 7). ACKNOWLEDGEMENTS The author wishes to thank Tatjana Unuk of Maribor Slovenia for the data on Gala apple clour distributions in the orchard and Pol Tijskens for analyzing the data. Literature Cited Hertog, M.L.A.T.M (2002) The impact of biological variation on postharvest population

dynamics. Postharvest Biology and Technology Vol. 26 (3), 253-263 Hertog, M.L.A.T.M., Jeroen Lammertyn, Nico Scheerlinck and Bart M. Nicolaï (2007)

The impact of biological variation on postharvest behaviour: The case of dynamic temperature conditions. Postharvest Biology and Technology Vol. 43 (2), 183-192

Kooten, O. van, Heuvelink, E. and Stanghellini, C. (2008) New developments in greenhouse technology can mitigate the water shortage problem of the 21st century. Acta Hort. (ISHS) 767:45-52

Kooten, O. van and Kuiper, E. 2009. Consumer Acceptability in Flower Chains: How Can We Determine what the Final Customers Really Want?. Acta Hort. (ISHS) 847:17-26

Porter M.E. (1998) Competitive Advantage: creating and sustaining superior performance. Free Press, New York,

Reid M.S. (2002), How can we sell more flowers? In: U.C. Cooperative Extension, Davis, California

Schepers, H. and van Kooten, O. (2006) Profitability of ‘ready-to-eat’ strategies: Towards a model-assisted negotiation in a fresh produce chain. In: Quantifying the Agri-food Supply Chain. Ondersteijn, C.J.M., J.H.M. Wijnands, R.B.M. Huirne and O. van Kooten (eds.), pp. 117-132, Springer, The Netherlands


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Schouten R.E, L. M. M. Tijskensb and O. van Kooten (2002) Predicting keeping quality of batches of cucumber fruit based on a physiological mechanism. Postharvest Biology and Technology, Vol. 26 (2), 209-220

Schouten R.E., Tanja P.M. Huijben, L.M.M. Tijskens, Olaf van Kooten(2007b) Modelling the acceptance period of truss tomato batches, Postharvest Biology and Technology, Vol. 45(3), 307-316

Tijskens, L.M.M., Heuvelink, E., Schouten, R.E., Lana, M.M. and van Kooten, O. (2005). The Biological Shift Factor: Biological Age as a Tool for Modelling in Pre- and Postharvest Horticulture. Acta Hort. (ISHS) 687:39-46

Vorst, J.G.A.J. van der; Kooten, O. van; Marcelis, W.J.; Luning, P.A.; Beulens, A.J.M. (2007). Quality controlled logistics in food supply chain networks: integrated decision-making on quality and logistics to meet advanced customer demands In: Proceedings of the Euroma 2007 conference, Ankara, Turkey, 18-20 June 2007.

Figures

Figure 1: Property of a product as it develops through time from immature to mature to

unacceptable for consumption. The moment of harvest is usually before the product reaches the optimal state for consumption. Most properties either have this logistic development curve. However a logistic curve or even a linear curve also occurs (Hertog 2002).

Figure 2: A batch of products harvested from the same location at one point in time tends

to have a single virtual rate constant for the development process (temperature dependence according to Ahrrenius) but a different age for each single product. This age difference is expressed as t. What was discovered on many occasions is that this t is normally distributed (inset), allowing the development of a stochastic model out of the mechanistic model in the figure (Tijskens et al. 2005).


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Figure 3: A property of a batch of products as it develops logistically in time is measured

cross sectional at three points in the development (given by the arrows). The distribution of the property at these points is given by the insets. The distributions to the left and to the right are extremely skewed, while the distribution in the middle is normal. It follows that through the shape of the distribution in one cross sectional measurement it is possible to determine where the batch is in its development if the mechanistic model is known (Schouten et al. 2002).


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Figure 4: Distribution of colour on the a-axis of the Lab system as measured on the sunny side of Gala apple trees starting when the fruits are still very small till about one month before harvest. It is clear that the apples turn from green to red. The distribution starts extremely skewed to the green side and then via a normal distribution it turns very much skewed to the red side before harvest. (data courtesy of Tatjana Unuk from Maribor, Slovenia)

Figure 5: PlantLab® is a research unit for developing fully closed plant production systems. The conditions that can be controlled are shown in the picture. More information can be found at www.plantlab.nl


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Figure 6: The possibilities for fully closed plant production under artificial light are seemingly endless. Here is an artists view depicting plant production between the parking lot and the shopping mall in a big city. Direct supplying of different outlets are possible. It solves logistical problems and it allows for synchronization between supply and demand as the production period is precisely defined.

Figure 7: The unique set of parameters to be adjusted in the PlantLab Research Units contains: red, blue and farred, infra red, room temperature, humidity, airflow, root temperature, CO2 level, irrigation and nutrients. PlantLab Units are for sale, to be build up on site or in reefers by Imtech (the Netherlands) and than to be shipped world wide.

greenhouse horticulture in the 21st century; can we stay competitive?

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