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Beya-Marshall, V., Arcos, E., Seguel, O., Galleguillos, M., & Kremer, C. (2022). Optimal irrigation management for avocado (cv. 'Hass') trees by monitoring soil water content and plant water status. Agric. Water Manag., 271, 107794.
Abstract: Irrigation scheduling based on soil water content (Ow) sensors requires that Ow be maintained within a range (management lines) that is optimal for plant growth. The lower limit or “breaking point ” is determined following the soil water content dynamics on the transition of a rapid rate of depletion to a slower, under similar reference evapotranspiration. Although this criterion is practical, its implementation should be validated with plant water status measurement that contemplate weather condition, such as stem water potential “non-stressed ” baseline (Tx as a function of vapor-pressure deficit (VPD) in Ow conditions that do not limit yield). A study was con-ducted on a mature cv. 'Hass' avocado orchard in Central Chile during two seasons. There were 5 irrigation treatments: T1, Control; T2 and T3 with 29% less and 25% more of what was applied in T1, respectively; T4 and T5 same as Control until first and second fruit drop abscission, respectively, and then with 29% less. T1 trees were irrigated using a continuous frequency domain reflectometry (FDR) probe to maintain the root zone be-tween field capacity and the breaking point. There was biweekly monitoring of the Ow prior to irrigation, Tx and VPD. The Tx decline proportional to the intensity and the timing of water restriction; however, no treatment affected the crop load in either season. T2 did not show significant detrimental in fruit size, production and maturation, despite that frequently reached water content levels at the limit of the breaking point, and showed lower levels of stem water potential than Control, being the treatment with the highest water productivity. The results confirm that breaking point is an effective criterion to establish irrigation management. Additionally, when comparing the baseline for our non-stressed trees with a baseline from full irrigation treatments obtained from the literature, 30% water savings were achieved.
O' Ryan, R., Benavides, C., Diaz, M., San Martin, J. P., & Mallea, J. (2019). Using probabilistic analysis to improve greenhouse gas baseline forecasts in developing country contexts: the case of Chile. Clim. Policy, 19(3), 299–314.
Abstract: In this paper, initial steps are presented toward characterizing, quantifying, incorporating and communicating uncertainty applying a probabilistic analysis to countrywide emission baseline forecasts, using Chile as a case study. Most GHG emission forecasts used by regulators are based on bottom-up deterministic approaches. Uncertainty is usually incorporated through sensitivity analysis and/or use of different scenarios. However, much of the available information on uncertainty is not systematically included. The deterministic approach also gives a wide range of variation in values without a clear sense of probability of the expected emissions, making it difficult to establish both the mitigation contributions and the subsequent policy prescriptions for the future. To improve on this practice, we have systematically included uncertainty into a bottom-up approach, incorporating it in key variables that affect expected GHG emissions, using readily available information, and establishing expected baseline emissions trajectories rather than scenarios. The resulting emission trajectories make explicit the probability percentiles, reflecting uncertainties as well as possible using readily available information in a manner that is relevant to the decision making process. Additionally, for the case of Chile, contradictory deterministic results are eliminated, and it is shown that, whereas under a deterministic approach Chile's mitigation ambition does not seem high, the probabilistic approach suggests this is not necessarily the case. It is concluded that using a probabilistic approach allows a better characterization of uncertainty using existing data and modelling capacities that are usually weak in developing country contexts. Key policy insights Probabilistic analysis allows incorporating uncertainty systematically into key variables for baseline greenhouse gas emission scenario projections. By using probabilistic analysis, the policymaker can be better informed as to future emission trajectories. Probabilistic analysis can be done with readily available data and expertise, using the usual models preferred by policymakers, even in developing country contexts.