Vincent de Leijster

63 Almond farm profitability under agroecological management 4 Harvest measurements In each treatment of each experimental site we measured production during the harvest season, August–September, in 2016, 2017 and 2018. Per treatment 16 trees were harvested in groups of four, thus obtaining four replicates. Almond fruits were hulled and shelled to obtain almond yield expressed as kernel weight per tree. Detailed methodology is described in De Leijster et al. (2019) and production data are given in the Appendix Figure A4-1 and A4-2 and Table A4-3. Survey In the winter of 2015-2016 we conducted a survey among the same five farmers who owned the farms wherein the field experiments where conducted and an additional ten farmers (total n = 15). The aim of the survey was to characterize management practices and to obtain data on investment and operational costs, self-reported yield, farm-gate price, and other in-farm sources of income besides almond cultivation. The data was collected using semi- structured interviews that followed a pre-designed questionnaire. We collected data on (i) farm characteristics (farm area, crop age, crop density and almond variety), (ii) management characteristics (tillage frequency, pruning frequency, fertilizer type and quantity, pest treatment type and quantity, ground cover management), (iii) investment costs (on-site crop design, first time soil preparation and purchase and planting of trees), (iv) operational costs (tillage, soil amendment, ground cover management, pruning, pest control, machinery maintenance, diesel), (v) labor, and (vi) income (self-reported almond yield, farm gate almond price and subsidies). The operational costs obtained from this survey are presented in Table 4-2. 4.2.3 Model description and assumptions We used a stochastic cash flow model to simulate the economic performance of four management practices (CT, NT, GM and CM) for almond cultivation. Stochastic models allow for random variation over time in one or more input variables, for example yields, costs and market prices, making them an efficient tool to more realistically project farm cash flows (Richardson and Mapp, 1976). Stochastic models incorporate random variation using e.g. a Monte Carlo approach, requiring input values (averages) and information about the variance (standard deviations) that needs to be incorporated, and are increasingly used to project cash flows of farms and to compare the profitability of multiple management practices (Gobbi, 2000; Lalani et al., 2017; Yates et al., 2007). We used the stochastic cash flow model to project net present value (NPV), internal rate of return (IRR) and discounted payback time (DPBT), based on information from a combination of sources (Table 4-1). The model was implemented in Microsoft Office 365 Excel (version 1902).