Towards stochastic simulation of crop yield: a case study of fruit set in sweet pepper

Crop growth simulation models are widely used in research and education, and their use in commercial practice is increasing. Usually these models are deterministic: one set of input values always gives the same output of the model. In reality, however, variation exists between plants of the same crop. A simulation model taking this variation into account is therefore more realistic. The aim of this thesis is to introduce a stochastic component into a dynamic crop simulation model. As case study, fruit set in sweet pepper was used, because large variation in fruit set between the plants exists. Competition with fast growing fruits causes abortion of flowers and young fruits, which results in periods with high and low fruit set, and consequently periods of high and low fruit yield. A literature review showed that most factors influencing fruit abortion can be expressed in the terms source and sink strength. Source strength is the supply of assimilates; a higher source strength increases fruit set. Source strength takes into account leaf area, radiation, and CO2 level and temperature. Sink strength is the demand for assimilates of the fruits and vegetative parts. It is quantified by the potential growth rate, i.e. the growth rate under non-limiting assimilate supply. Assimilate demand of the fruits depends on their number, age, and cultivar. If the total fruit sink strength of a plant is low, fruit set is high. Vulnerable for abortion were very small buds, buds close to anthesis and flowers and young fruits up to 14 days after anthesis. An experiment with six Capsicum cultivars with fruit sizes ranging between 20 and 205g fresh weight showed that variation in weekly fruit yield is highly correlated with variation in weekly fruit set. Fruit yield patterns resembled fruit set patterns, with a lag time being equal to the average fruit growth duration. Further investigation showed that the cultivars not only differed in sink strength of the individual fruits, but also that the source-sink ratio above which fruit set occurred was higher in cultivars with larger fruits. In the second half of the thesis, flower and fruit abortion was modelled. Survival analysis was used as the method to derive the abortion function. Source and sink strength were used as the factors influencing abortion. Their effect on the probability of abortion per day was non-linear: at high values of source and sink strength an increase did not further decrease or increase the probability of abortion, respectively. Flowers on the side shoots turned out to have a higher probability of abortion than flowers on the main shoot. Most flowers and young fruits aborted around 100°Cd after anthesis. The obtained function was used in a crop simulation model for sweet pepper. After calibration the model was able to simulate the observed fruit set pattern, although fruit abortion was not properly simulated when low source strength was combined with high sink strength. Validation with three independent data sets gave reasonable to good results. Survival analysis proved to be a good method for introducing stochasticity in crop simulation models. A case study with constant source strength showed asynchronisation of fruit set between the plants, indicating that fluctuations in source strength are an important factor causing synchronisation between individual plants. <br/

Stochastic dynamic simulation of fruit abortion: a case study of sweet pepper

Abortion of reproductive organs diminishes yields in many crops. In indeterminate greenhouse crops, alternating periods of fruit abortion and fruit set exist, resulting in fluctuations in fruit yield. Factors affecting the level of abortion are e.g., the supply and demand for assimilates (source and sink strength, respectively), temperature and cultivar. However, simulation of fruit abortion is still a weak part of crop simulation models. Variation in fruit abortion exists between plants, which results in differences in the timing and the number of set fruits. Therefore, simulating fruit abortion with variation could give more realistic simulation results. The probability of a fruit to abort should be related to factors like source strength and sink strength. The more favourable the circumstances are for fruit abortion, e.g., low source strength or high sink strength, the more likely it is that the fruit aborts. Survival analysis estimates parameters quantifying the influence of explanatory variables on the abortion rate. Time-varying explanatory variables can be used in the analysis. In a case study, we used survival analysis to analyse a data set with observations on flowering, fruit abortion and fruit harvest for sweet pepper. Source and sink strength were used as explanatory variables. The resulting equation determining the probability of abortion per day was implemented in a simple simulation model to simulate fruit set. The model output, as an average of 100 plants, showed similar timing in the fluctuations in fruit set as the observations, although the amplitude of the fluctuations was in some cases underestimated. The percentage fruit set was simulated correctl

Input levels and intercropping productivity: exploration by simulation

Voorstudie van het effect van verschillende niveaus van waterinput op een sorghum-cowpea intercro

Survival analysis of flower and fruit abortion in sweet pepper

In order to obtain a crop growth model that can simulate inter- and intra-plant variation in fruit set, fruit abortion times in sweet pepper were analysed by means of survival analysis. Survival analysis is a statistical technique dealing with the timing of events. The Cox proportional hazards model estimates the instantaneous baseline probability of abortion per time (hazard rate), given that the fruit has not aborted yet. It also estimates the effect of explanatory factors (covariates) on the abortion rate. Two important factors known to influence abortion in sweet pepper are the supply and demand for assimilates (source and sink strength, respectively). A plant density experiment was analysed, as density influences plant source and indirectly also sink strength. Flowering as well as abortion or harvest dates were recorded for all individual flowers and fruits. LAI was measured to calculate the source strength with a photosynthesis-based simulation model. Empirical curves showed that survival of flowers and fruits (per plant) was higher at 1.6 plants/m2 than at 3.1 or 4.6 plants/m2. Plant density, source strength and source/sink ratio all correlated with the treatment plant density, but source/sink ratio explained most of the variation in the data in a Cox proportional hazards model. Averaging the source/sink ratio over seven days gave the best fit. Adding the position within the node (main or side branch) improved the fit; flowers from the side branch had a higher probability of abortion per unit time than flowers from the main branch. The susceptibility for fruit abortion differed among plants. The baseline hazard rate indicated that between 0 and 13 days after anthesis, flowers were most susceptible for abortion

QTL analyses on genotype-specific component traits in a crop simulation model for capsicum annuum L.

Abstract: QTL for a complex trait like yield tend to be unstable across environments and show QTL by environment interaction. Direct improvement of complex traits by selecting on QTL is therefore difficult. For improvement of complex traits, crop growth models can be useful, as such models can dissect a target trait into a number of component traits. QTL for the component traits are assumed to be more stable across environments. The target trait can be reconstructed from its component traits together with environmental inputs. Instead of observed component traits, QTL fits for component traits may be used when QTL explain a reasonable proportion of the variation in the components. We applied this dissection approach to the target trait total shoot biomass for a population of 149 recombinant inbred lines from the intraspecific cross of Capsicum annuum ‘Yolo Wonder’ and ‘Criollo de Morelos 334’. A simple LINTUL-type simulation model was used, with rate of change of leaf area index and light use efficiency as genotype specific component traits. These two component traits were determined in four phenotyping experiments (spring and autumn cultivations in the Netherlands and Spain), and subjected to QTL analysis. Seven QTL were found for both component traits. For leaf area index development rate 40 to 50% of the observed variance was explained by the QTL, while this was slightly lower for light use efficiency (23-39%). Using the QTL fitted values of the component traits following QTL analysis, the crop simulation model explained 27-43% of the observed variation in total shoot biomass, which was higher than the variation explained by the QTL for total shoot biomass itself for most experiments. The approach of dissecting a complex trait into its component traits is therefore a promising one. Next step is to extend the model with biomass partitioning

Virtuele roos: experimenteel en modelmatig onderzoek naar gewasopbouw roos

De gewasopbouw of gewasstructuur is een belangrijke bepalende factor voor productie en kwaliteit bij roos. Met name het uitlopen van een okselknop en en de daarop volgende uitgroei tot bloemscheut hangen nauw samen met de gewasstructuur. De ideale gewasopbouw is niet gelijk voor alle rassen, terwijl nieuwe rassen elkaar in snel tempo opvolgen. Ook de ontwikkelingen op het gebied van robotisering en mobiele teeltsystemen gaan nieuwe eisen stellen aan de gewasopbouw. De gewasopbouw zal zodanig moeten zijn dat het enerzijds voldoet aan de eisen van de techniek en dat anderzijds een optimale productie en kwaliteit geleverd worden. Dit vraagt om keuzen te maken in teeltstrategieën. Gewasopbouw is een complex proces dat niet los gezien kan worden van plantverband, raseigenschappen, snoeistrategie en klimaat. Om hier meer grip op te kunnen krijgen is een aantal proeven uitgevoerd naar verschillen in plantopbouw. Tevens is een gewasgroeimodel ontwikkeld dat de gewasontwikkeling en gewasopbouw (in 3 dimensies) kan berekenen

Growth and nutrient absorption of Cape Gooseberry (Physalis Peruviana L.) in soilless culture

"This is an Author's Accepted Manuscript of an article published in [include the complete citation information for the final version of the article as published in the Journal of Plant Nutrition 2015 March, available online at: http://www.tandfonline.com/10.1080/01904167.2014.934474."Cape gooseberry (Physalis peruviana L.) is a solanaceous plant. The growth and time-course of nutrient accumulation of the plant and its partitioning between roots, stems, leaves, and fruits were examined. The study was conducted analyzing two nutrient solutions in soilless culture under greenhouse conditions during two consecutive seasons. The macronutrient contents were analyzed. On average, the yield was 8.9 t.ha(-1). Growth of the plant until 90 d after transplanting obeys an exponential function of time and the relative growth rate for this period was determined. Nitrogen (N) was the element that showed the highest concentration, corresponding to leaves (4.67%), followed by potassium (K) in stems (4.46%). The highest accumulations of N, phosphorous (P), calcium (Ca), and magnesium (Mg) were found in leaves and of K in the stems. Potassium showed the highest nutrient accumulation (29 g.plant(-1)) and the highest specific uptake rate.Torres Rubio, JF.; Pascual Seva, N.; San Bautista Primo, A.; Pascual España, B.; López Galarza, SV.; Alagarda Pardo, J.; Maroto Borrego, JV. (2015). Growth and nutrient absorption of Cape Gooseberry (Physalis Peruviana L.) in soilless culture. Journal of Plant Nutrition. 38(4):485-496. doi:10.1080/01904167.2014.934474S485496384Bellaloui, N., & Brown, P. H. (1998). Plant and Soil, 198(2), 153-158. doi:10.1023/a:1004343031242Bennett, J. P., Oshima, R. J., & Lippert, L. F. (1979). Effects of ozone on injury and dry matter partitioning in pepper plants. Environmental and Experimental Botany, 19(1), 33-39. doi:10.1016/0098-8472(79)90022-4CAUSTON, D. R. (1991). Plant Growth Analysis: The Variability of Relative Growth Rate Within a Sample. Annals of Botany, 67(2), 137-144. doi:10.1093/oxfordjournals.aob.a088112Convenio MAG-IICA (Ministerio de Agricultura y Ganadería. Institución Interamericana de Cooperación para la Agricultura). 2001. The cape gooseberry (Physalis peruvianaL.Physalis edulis). Subprograma de Cooperación Técnica, Ecuador. Available at: http://www.sica.gov.ec/agronegocios/Biblioteca/Convenio%20MAG%20IICA/productos/uvilla_mag.pdf (Accessed July 2007, in Spanish).El-Tohamy, W. A., El-Abagy, H. M., Abou-Hussein, S. D., & Gruda, N. (2009). Response of Cape gooseberry (Physalis peruviana L.) to nitrogen application under sandy soil conditions. Gesunde Pflanzen, 61(3-4), 123-127. doi:10.1007/s10343-009-0211-0Fresquet, J., Pascual, B., López-Galarza, S., Bautista, S., Baixauli, C., Gisbert, J. M., & Maroto, J. V. (2001). Nutrient uptake of pepino plants in soilless cultivation. The Journal of Horticultural Science and Biotechnology, 76(3), 338-343. doi:10.1080/14620316.2001.11511373Heuvelink, E., Bakker, M. J., Elings, A., Kaarsemaker, R. C., & Marcelis, L. F. M. (2005). EFFECT OF LEAF AREA ON TOMATO YIELD. Acta Horticulturae, (691), 43-50. doi:10.17660/actahortic.2005.691.2Leskovar, D. I., & Cantliffe, D. J. (1993). Comparison of Plant Establishment Method, Transplant, or Direct Seeding on Growth and Yield of Bell Pepper. Journal of the American Society for Horticultural Science, 118(1), 17-22. doi:10.21273/jashs.118.1.17Marcelis, L. F. M. (1993). Fruit growth and biomass allocation to the fruits in cucumber. 1. Effect of fruit load and temperature. Scientia Horticulturae, 54(2), 107-121. doi:10.1016/0304-4238(93)90059-yPuente, L. A., Pinto-Muñoz, C. A., Castro, E. S., & Cortés, M. (2011). Physalis peruviana Linnaeus, the multiple properties of a highly functional fruit: A review. Food Research International, 44(7), 1733-1740. doi:10.1016/j.foodres.2010.09.034Radford, P. J. (1967). Growth Analysis Formulae - Their Use and Abuse1. Crop Science, 7(3), 171. doi:10.2135/cropsci1967.0011183x000700030001xRamadan, M. F., & Moersel, J. T. (2007). Impact of enzymatic treatment on chemical composition, physicochemical properties and radical scavenging activity of goldenberry (Physalis peruviana L.) juice. Journal of the Science of Food and Agriculture, 87(3), 452-460. doi:10.1002/jsfa.2728Ramadan, M. F., & Moersel, J.-T. (2009). Oil extractability from enzymatically treated goldenberry (Physalis peruvianaL.) pomace: range of operational variables. International Journal of Food Science & Technology, 44(3), 435-444. doi:10.1111/j.1365-2621.2006.01511.xSalazar, M. R., Jones, J. W., Chaves, B., & Cooman, A. (2008). A model for the potential production and dry matter distribution of Cape gooseberry (Physalis peruviana L.). Scientia Horticulturae, 115(2), 142-148. doi:10.1016/j.scienta.2007.08.015Scholberg, J., McNeal, B. L., Jones, J. W., Boote, K. J., Stanley, C. D., & Obreza, T. A. (2000). Growth and Canopy Characteristics of Field-Grown Tomato. Agronomy Journal, 92(1), 152. doi:10.2134/agronj2000.921152xTrinchero, G. D., Sozzi, G. O., Cerri, A. M., Vilella, F., & Fraschina, A. A. (1999). Ripening-related changes in ethylene production, respiration rate and cell-wall enzyme activity in goldenberry (Physalis peruviana L.), a solanaceous species. Postharvest Biology and Technology, 16(2), 139-145. doi:10.1016/s0925-5214(99)00011-3Turner, A. (1994). Dry Matter Assimilation and Partitioning in Pepper Cultivars Differing in Susceptibility to Stress-induced Bud and Flower Abscission. Annals of Botany, 73(6), 617-622. doi:10.1006/anbo.1994.1077WILLIAMS, R. F. (1946). The Physiology of Plant Growth with Special Reference to the Concept of Net Assimilation Rate. Annals of Botany, 10(1), 41-72. doi:10.1093/oxfordjournals.aob.a083119Zapata, J.L., A. Saldarriaga, M. Londoño, and C. Díaz. 2002. Cape gooseberry Management in Colombia. Antioquia, Colombia: Rionegro, Programa Nacional de Transferencia de Tecnología Agropecuaria - Corpoica Regional Cuatro (in Spanish).Zerihun, A. (2000). Compensatory Roles of Nitrogen Uptake and Photosynthetic N-use Efficiency in Determining Plant Growth Response to Elevated CO2: Evaluation Using a Functional Balance Model. Annals of Botany, 86(4), 723-730. doi:10.1006/anbo.2000.123

Source/sink-verhouding tijdens bloei bepalend voor zetting paprika : verhouding tussen source (suikerproductie) en sinks (suikerbenutting)

Fluctuaties in zetting en oogst horen bij paprika. Meer zicht op de oorzaken daarvan zou de veredelaars de instrumenten in handen geven om te komen tot een stabieler ras. Nieuw Wagenings onderzoek biedt perspectief daarvoor. De verhouding tussen source (suikerproductie) en sinks (suikerbenutting) blijkt van belang. Rond de bloei moet die verhouding boven een bepaalde drempel liggen anders aborteert de bloem of jonge vrucht. De teler kan helaas niets aan die drempel doen; de veredelaar wellicht wel

Abortion of reproductive organs in sweet pepper (Capsicum annuum L.): a review

Levels of abortion of reproductive organs (i.e., buds, flowers, and young fruits) in sweet pepper plants (Capsicum annuum L.) are high, and cyclical fluctuations occur in fruit set. Stages susceptible to abortion are very young buds (<2.5 mm), buds close to anthesis, and flowers and fruits up to 14 d after anthesis. An overview of factors and processes involved in flower and fruit abortion in sweet peppers is presented. More light, higher CO2 concentrations, and lower planting density, increase the availability of assimilates per plant, and decrease fruit abortion. The cyclical pattern in fruit set is caused by changes in demand for assimilates. High flower abortion occurs when fast growing fruit (at approx. 3 weeks after anthesis) are present, due to competition for assimilates. Fruit set increases when fast growing fruit are almost mature and have a low assimilate demand. Prior to abortion, auxin export from the reproductive organ diminishes, ethylene production increases, and lower levels of activity of sucrose-cleaving enzymes are found. Severe water stress and low nutrient supply also increase abortion levels. Low night- and high day-time temperatures hamper pollen development, causing low seed set, which can result in fruit abortion. Two theories have been used to explain abortion: unbalanced demand for and supply of assimilates, and hormonal dominance of developing fruit over young fruit. Attempts to prevent abortion or to diminish the cyclical pattern of fruit set have not yet been successful, but new suggestions are presented

Quantifying abortion rates of reproductive organs and effects of contributing factors using time-to-event analysis

Time-to-event analysis, or survival analysis, is a method to analyse the timing of events and to quantify the effects of contributing factors. We apply this method to data on the timing of abortion of reproductive organs. This abortion often depends on source and sink strength. We hypothesise that the effect of source and sink strength on abortion rate can be quantified with a statistical model, obtained via survival analysis. Flower and fruit abortion in Capsicum annuum L., observed in temperature and planting density experiments, were analysed. Increasing the source strength as well as decreasing the sink strength decreased the abortion rate. The effect was non-linear, e.g. source strengths above 6 g CH2O per plant per d did not decrease abortion rates further. The maximum abortion rate occurred around 100 degree-days after anthesis. Analyses in which sink strength was replaced with the number of fruits in a specified age category had an equal or better fit to the data. We discuss the advantages and disadvantages of using survival analyses for this kind of data. The technique can also be used for other crops showing reproductive organ abortion (e.g. soybean (Glycine max L.), cucumber (Cucumis sativus L.)), but also on other event types like bud break or germination