Comparison of neutron scattering and DFM capacitance instruments in measuring soil water evaporation

Soil water evaporation is an important parameter that needs to be accurately measured for the design of water-efficient agricultural systems. With this study, the abilities of the DFM capacitance probes and a neutron water meter (NWM) to measure evaporation from the soil surface were compared. Measured evaporation was compared to the control values measured with mini-lysimeters. Calibration of DFM capacitance probes and the NWM was done in the laboratory using the topsoil of a Bainsvlei soil form. Field measurements of soil water content were done on the same Bainsvlei soil. Calibration results indicated a good correspondence (r2 = 0.99) between the measured values and known volumetric soil water contents. There was no significant difference (p = 95%) between the DFM evaporation measurements and the control, whereas the NWM and control differed significantly. It was concluded that the DFM capacitance probe is a better tool than the NWM in measuring evaporation from the topsoil surface.Keywords: neutron water meter, capacitance probes, evaporation, soil wetnes

Characterization of rainfall in the central South African Highveld for application in water harvesting

In-field rainwater harvesting (IRWH), a runoff farming system, is a beneficial water management technique for crop production in arid and semi-arid areas. In-field rainwater harvesting is influenced by rainfall characteristics, and hence this study aimed to identify and characterize rainfall events, and determine rainfall parameters that were of significance in in-field runoff. Two algorithms of event identification were developed. The algorithm that identified events spanning over a 24-h day limit as a single event, gave better identification results which were then characterized. This enabled systematic grouping of rainfall parameters. About 33% of the total rainfall amount received had zero potential to be harvested as runoff in the IRWH system. Therefore, a runoff harvesting practice needs to use the remaining 67%. Rainfall events that lasted 30 min or longer were of water-harvesting importance. This could be the minimum duration guideline when simulating rainfall for rainwater harvesting studies. Rainfall event amount and intensity were of significant importance for IRWH runoff determination

Characterization of rainfall in the central South African Highveld for application in water harvesting

In-field rainwater harvesting (IRWH), a runoff farming system, is a beneficial water management technique for crop production in arid and semi-arid areas. In-field rainwater harvesting is influenced by rainfall characteristics, and hence this study aimed to identify and characterize rainfall events, and determine rainfall parameters that were of significance in in-field runoff. Two algorithms of event identification were developed. The algorithm that identified events spanning over a 24-h day limit as a single event, gave better identification results which were then characterized. This enabled systematic grouping of rainfall parameters. About 33% of the total rainfall amount received had zero potential to be harvested as runoff in the IRWH system. Therefore, a runoff harvesting practice needs to use the remaining 67%. Rainfall events that lasted 30 min or longer were of water-harvesting importance. This could be the minimum duration guideline when simulating rainfall for rainwater harvesting studies. Rainfall event amount and intensity were of significant importance for IRWH runoff determination

Effect of Eucalyptus-Wood-Based Compost Application Rates on Avocado (<i>Persea americana</i> Mill) Foliar Nutrient Content and Fruit Yield

Background: The effects of different fertilizer types and their application rates on leaf nutrient content and avocado yield are unclear. An evaluation of eucalyptus-wood-based compost applied at 0, 5, 10, and 15 t ha−1 year−1 on foliar nutrient content, yield, and fruit size distribution (%) was completed at Mooketsi and Politsi, Limpopo Province, South Africa, from 2016 to 2018. Methods: A completely randomized block design with three replicates was used. Data were collected annually, and fruit size was classified as classes; 1 (>275 g), 2 (197–274 g), 3 (148–196 g), and 4 (0–147 g). Results: Leaf N, P, K, Ca, Mg, Fe, Mn, Na, Cu, and Zn, fruit yield and size distribution (%) were significantly (p −1 application rate, which corresponded to the highest yield (19.6 t ha−1) and the largest proportion of fruits in class 1. Conclusions: The ≥10 t ha−1 year−1 application rate can be used, though the compost saturation levels of the soils should be determined to avoid possible danger of nutrient toxicity if high application rates are continuously used for >3 years

Prediction of soil distribution on two soilscapes in land type Dc17 east of Bloemfontein, South Africa

The predictive nature of digital soil mapping makes it a labour- and cost-effective way of facilitating soil surveys. A digital elevation model was used to generate terrain attributes that can be used to infer the distribution of soil associations relative to the topography. Two study areas – Gladstone and Potsane – in the Free State Province of South Africa were considered. Slope, aspect, contour and plan curvature, topographic wetness index and topographic morphological unit were used to develop a model for predicting soil associations. Discriminant analysis was employed to develop the model. The model was trained on data obtained from Gladstone and validated on data from Gladstone and Potsane. Predicting soil form was unsatisfactory. Prediction done on soil associations, with soils grouped as deep, shallow and valley-bottom soils (criteria closely related to the suitability for in-field rainwater harvesting), achieved acceptable improvement in prediction accuracy. For Gladstone, when analysis was done using equal prior probability, accuracy percentages of 56.9%, 51.5% and 58.3% were found for calibration, cross-validation and areas suited to in-field rainwater harvesting, respectively. With prior probability set in accordance to sample frequency, the accuracy percentages were improved to 83.1%, 80.0% and 94.6%, respectively. In Potsane, the prediction accuracy percentage was low (38.23%) with equal prior probability but markedly improved (67.65%) when prior probability was similar to sample frequency. These results support the validity of the statement that the predictive nature of digital soil mapping makes it a labour- and cost-effective way of facilitating soil surveys