Blumenstein-etal-2016: OSCAR: Production economic implications of intercropping and mulching systems - An interdisciplinary application of Monte Carlo simulations.

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Benjamin ^Blumenstein1, Maria R. ^Finckh2, Detlev ^Möller1, Jan-Hendrik ^Schmict2, Raphael ^Wittwer3

Key words: production economics, interdisciplinary modeling, Monte Carlo simulation.

Abstract

Intercropping and mulching systems as well as reduced tillage can cause multiple positive ecological effects, which increase resilience and yield stability of cash crops in both organic and conventional farming. In this paper, a production-economic evaluation of corresponding crop rotations with consideration of yield effects is carried out by means of stochastic risk analysis based on empirical research results. A temporal extension of the observation horizon (long-term effects) could clarify the risk-reducing potential.

1  Introduction

Particularly in organic farming systems, in view of the limited possibilities of using means of production with short-term effects (fertilization, plant protection), special emphasis must be placed on prophylactically ensuring yield-stabilizing and possibly yield-increasing conditions through appropriately configured production processes. Frequently discussed possibilities are the establishment of mixed cultures in the sense of intercropping and mulch systems (HARTWIG AND AMMON 2002) or reduced tillage systems (e.g. PITTELKOW ET AL. 2015) with different ecological, but also economic effects. In this context, a managerial, business-oriented evaluation is dependent on close cooperation with experimentally working groups. Especially the strong dependence on local, site-specific conditions, the wide range of results from field experiments with a high dependence on annual effects makes it difficult to define a robust, deterministically derived recommendation for action. In the presented approach, an explicit consideration of stochastic effects by means of Monte Carlo simulation is considered to have the potential to provide more realistic scientific and practical arguments. System theoretical considerations help to establish a level of communication between the actors in an inter- but also transdisciplinary context and thus to achieve a strong foundation of the business analysis.

2  Material and Methods

Empirical data collection on the use of intercropping/mulching systems under conventional (KB) and reduced (RB) tillage in biennial crop rotations (EU project OSCAR: Optimising Subsidiary Crop Applications in Rotations; 2012-2016) form the basis of the economic aspects presented here. The trials of the University of Kassel (D) (ÖKO: winter wheat-potato) and Agroscope Zurich (CH) (KONV: winter wheat-grain maize) are considered in this paper. The basis of the risk simulation of the crop rotation systems is the calculation of the direct and labor cost free output (DAKL) based on KTBL standard data. Using @RISK (PALISADE 2010), probability functions for yield parameters of cash crops and straw (ART; KU) and harvest and storage losses (KU) were estimated from the underlying field experiments and selected based on χ²-statistics (ART per eight data sets; KU each 16 data sets; Weibull, Gamma, Beta-General, Pearson or LogLogistic distribution depending on the data set) to be used in the Monte Carlo simulation to represent probability distributions of DAKL.

3  Results and discussion

Depending on the tillage system, the excellence of the CH intercropping systems (shagbark vetch, oil radish, ground clover, control without ZF) varies in comparison. In KB, the control dominates almost all other practices, but not completely, which is why only Second Degree Stochastic Dominance (SDZ) is present here. In RB the cultivation system shagbark vetch completely dominates all other systems, so there is Stochastic Dominance First Degree (SDE). In the no-till system, the cultivation system shagbark vetch shows SDZ and would thus be preferable to the other systems from the objective point of view of the risk analysis results. If the subjective risk attitude of decision makers is included, shagbark vetch would also be preferable from the perspective of risk neutral or risk averse decision makers. However, if the expected-value-variance (μ,σ) principle is taken into account, a risk-averse decision maker would possibly choose control, since this system promises a better economic outcome than shaggy vetch in 22% of the cases. At site D, the KB systems dominate their respective RB mulch or intercropping systems in terms of their expected or maximum values. However, the dispersion of values is lower in the RB systems, accompanied by higher yield stability and lower risk potential. In both conv. and organic OSCAR cropping systems, the shagbark vetch systems are dominant. While in organic systems RB shows lower yield variability with a tendency to lower risk potential, conventional systems with RB show higher risk potential. The dispersion of DAKL values is higher in the organic than in the conventional variants (reason: market crop potato with basically higher risk potential).

4  Conclusions

Conclusive statements on the economic advantageousness of individual
tillage or intercropping/mulch systems are difficult to derive at present, since profitability and risk portfolio must be seen in the context of the individual very different OSCAR trial sites in each case. In future research, in addition to stochastic effects, the time horizon will increasingly have to be taken into account, because trade-offs between short-term and medium- and long-term effects are highly relevant for practical decision makers and possible win-win situations have to be identified (cf. WEINER, 2003). Considering long-term effects (nutrient availability, soil fertility) of RB and intercropping/mulching systems could increase profitability in the longer term and lead to a reduction of risk potential through yield stabilizing effects.

Literature

HARTWIG, N.L. AND AMMON, H.U. (2002): Cover crops and living mulches. Weed Science 50 (6): 688-699
PALISADE (2010): User manual for @RISK 5.7, risk and simulation add-in for Excel. Palisade Corporation. Ithaca, NY, USA
PITTELKOW, C.M. et al. (2015): Productivity limits and potentials of the principles of conservation agriculture. Nature 517: 365-368
WEINER, J. (2003): Ecology - the science of agriculture in the 21st century. The Journal of Agricultural Science 141 (3-4): 371

[1]University of Kassel, Department of Organic Agricultural Sciences, Business Administration, Steinstr. 19, D-37213 Witzenhausen , blumenst@uni-kassel.de, www.uni-kassel.de/agrar/bwl

[2]University of Kassel, Department of Organic Agricultural Sciences, Department of Organic Plant Protection, Nordbahnhofstr. 1a, D-37213 Witzenhausen, Germany

[3]Agroscope, Zurich, Switzerland, Institute of Sustainability Sciences, Plant-Soil Interaction Group.