Irrigation scheduling for potatoes (Solanum tuberosum L.) under drip irrigation in an arid region using AquaCrop model.

Potato is one of the key food crops and China is the largest potato producer in the world. However water scarcity is the major constraint to increase the productivity of potato in the arid regions such as Ningxia in northwest China where this crop is extensively cultivated. The overall objective of this study was to optimize the irrigation for potato cultivated under the drip irrigation. To do this, the AquaCrop model was calibrated and validated using the data obtained from two years of field experiment. Then, the calibrated crop model was used to simulate growth and tuber yield of potato in response to 30 different irrigation schemes under two different irrigation scenarios. The crop model evaluation parameters namely, the root mean square error (RMSE), the index of agreement (d), the normalized root mean square error (NRMSE) and the coefficient of determination (R2) showed that the AquaCrop model could simulate the growth and yield of potato under the drip irrigation with different irrigation treatments with reasonable accuracy. Furthermore, yield of potato has increased with increasing amount of total irrigation under drip irrigation; however, yield begins to decline when the amount of total irrigation exceeds 2500 m3 ha-1. The study also found that the optimum irrigation schedule for potato was 20 mm of irrigation quota at 7 days of irrigation cycle (i.e., 1800 m3 ha-1 or 180 mm of total irrigation). The above irrigation scheduling has achieved 46.77 t ha-1 of tuber yield with 15.74 kg m-3 of water use efficiency. These findings may be evaluated in potato cultivation across different climate and soil conditions for wide applicability at different arid regions of the world.

Application of uniform design to evaluate the different conditions on the growth of algae Prymnesium parvum.

Prymnesium parvum is an environmentally harmful algae and well known for its toxic effects to the fish culture. However, there is a dearth of studies on the growth behavior of P. parvum and information on how the availability of nutrients and environmental factors affect their growth rate. To address this knowledge gap, we used a uniform design approach to quantify the effects of major nutrients (N, P, Si and Fe) and environmental factors (water temperature, pH and salinity) on the biomass density of P. parvum. We also generated the growth model for P. parvum as affected by each of these nutrients and environmental factors to estimate optimum conditions of growth. Results showed that P. parvum can reach its maximum growth rate of 0.789, when the water temperature, pH and salinity is 18.11 °C, 8.39, and 1.23‰, respectively. Moreover, maximum growth rate (0.895-0.896) of P. parvum reached when the concentration of nitrogen, phosphorous, silicon and iron reach 3.41, 1.05, 0.69 and 0.53 mg/l, respectively. The order of the effects of the environmental factors impacting the biomass density of P. parvum was pH > salinity > water temperature, while the order of the effects of nutrients impacting the biomass density of P. parvum was nitrogen > phosphorous > iron > silicon. These findings may assist to implement control measures of the population of P. parvum where this harmful alga threatens aquaculture industry in the waterbodies such as Ningxia region in China.

New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems.

For the estimation of the soil organic carbon stocks, bulk density (BD) is a fundamental parameter but measured data are usually not available especially when dealing with legacy soil data. It is possible to estimate BD by applying pedotransfer function (PTF). We applied different estimation methods with the aim to define a suitable PTF for BD of arable land for the Mediterranean Basin, which has peculiar climate features that may influence the soil carbon sequestration. To improve the existing BD estimation methods, we used a set of public climatic and topographic data along with the soil texture and organic carbon data. The present work consisted of the following steps: i) development of three PTFs models separately for top (0-0.4 m) and subsoil (0.4-1.2 m), ii) a 10-fold cross-validation, iii) model transferability using an external dataset derived from published data. The development of the new PTFs was based on the training dataset consisting of World Soil Information Service (WoSIS) soil profile data, climatic data from WorldClim at 1 km spatial resolution and Shuttle Radar Topography Mission (SRTM) digital elevation model at 30 m spatial resolution. The three PTFs models were developed using: Multiple Linear Regression stepwise (MLR-S), Multiple Linear Regression backward stepwise (MLR-BS), and Artificial Neural Network (ANN). The predictions of the newly developed PTFs were compared with the BD calculated using the PTF proposed by Manrique and Jones (MJ) and the modelled BD derived from the global SoilGrids dataset. For the topsoil training dataset (N = 129), MLR-S, MLR-BS and ANN had a R2 0.35, 0.58 and 0.86, respectively. For the model transferability, the three PTFs applied to the external topsoil dataset (N = 59), achieved R2 values of 0.06, 0.03 and 0.41. For the subsoil training dataset (N = 180), MLR-S, MLR-BS and ANN the R2 values were 0.36, 0.46 and 0.83, respectively. When applied to the external subsoil dataset (N = 29), the R2 values were 0.05, 0.06 and 0.41. The cross-validation for both top and subsoil dataset, resulted in an intermediate performance compared to calibration and validation with the external dataset. The new ANN PTF outperformed MLR-S, MLR-BS, MJ and SoilGrids approaches for estimating BD. Further improvements may be achieved by additionally considering the time of sampling, agricultural soil management and cultivation practices in predictive models.

Biochemical Composition and Expression of Anthocyanin Biosynthetic Genes of a Yellow Peeled and Pinkish Ariled Pomegranate ( Punica granatum L.) Cultivar are Differentially Regulated in Response to Agro-Climatic Conditions.

The accumulation of beneficial biochemical compounds in different parts of pomegranate ( Punica granatum L.) fruit determines fruit quality and highly depends on environmental conditions. We investigated the effects of agro-climatic conditions on major biochemical compounds and on the expression of major anthocyanin biosynthetic genes in the peels and arils of a yellow-peeled and pink-ariled pomegranate cultivar in three agro-climatologically different locations in Sri Lanka. Drier and warmer climates promoted the accumulation of the measured biochemical compounds, i.e. total phenolic content (TPC), antioxidant capacity (AOX), and α, ß, and total punicalagin, in both peels and arils compared to wetter and cooler climates. Pomegranate DFR, F3H, and ANS transcripts in both peels and arils showed higher relative expression in hotter and drier regions, compared to those grown in cooler and wetter conditions. Therefore, growing pomegranates in drier and warmer environments maximizes the production of beneficial biochemical compounds and associated gene expression in pomegranate fruit.