Abstract:Two major criteria in choosing climate data for use in hydrological modelling are the period of record of the data set and the proximity of the collection platform(s) to the basin under study. Conventional data sets are derived from weather stations; however, in many cases there are no weather stations sufficiently close to a basin to be representative of climate conditions in that basin. In addition, it is often the case either that the period of record for the weather station(s) does not cover the period of the proposed simulation or that there are gaps in the data. Therefore, the objectives of this study are to investigate alternative climate data sources for use in hydrological modelling and to develop a protocol for creating hydrological data sets that are spatially and temporally harmonized. The methods we used for constructing daily, spatially distributed, climatic data sets of precipitation, maximum and minimum temperature, wind speed, solar radiation, potential evapotranspiration, and relative humidity are described. The model used in this study was the Soil and Water Assessment Tool implemented on the Mimbres River Basin located in southwestern New Mexico, USA, for the period 2003-2006. Our hydrological simulations showed that two events in January and February 2005 were missed, while an event in August 2006 was well simulated. We have also investigated the usefulness of several other precipitation data sets and compared the simulation results.

IntroductionGeographic information system (GIS) based distributed hydrologic models simulate the hydrologic processes using spatial parameters derived from geospatial data. These data mainly have information about relief, soil and land cover types, and intensity. Land cover has a great impact on the water quantity and quality in a river basin. Better estimation of land cover parameters improves the performance of the hydrologic model used. Appropriate spatial and temporal resolution of the used land cover improves the prediction of the hydrologic model (Huang et al., 2013). Several studies have been conducted to study the impact of land cover change on hydrology and water quality by (1) using readily available data (Cai et al., 2012;Yan et al., 2013), (2) using artificial land cover scenarios including farming practices (Chaplot et al., 2004;De Girolamo and Lo Porto, 2012;Mbonimpa et al., 2012), and (3) generating land use change scenarios using the land use change models (one such land use change model is the conversion of land use and its effects model (CLUE-s, Verburg et al.,

Fertilizer nitrogen (N) management in any region following standard general recommendations discount the fact that crop response to N varies between sites and seasons. To devise field-specific N management in wheat at jointing stage (Feekes 6 growth stage) using atLeaf meter and leaf colour chart (LCC), eight field experiments were conducted in three wheat seasons during 2017–2020 in the West Delta of Egypt. In the first two seasons, four experiments consisted of treatments with a range of fertilizer N application levels from 0 to 320 kg N ha−1. Monitoring atLeaf and LCC measurements at Feekes 6 growth stage in plots with different yield potentials allowed formulation of different criteria to apply field-specific and crop need-based fertilizer N doses. In the four experiments conducted in the third season in 2019/20, different field-specific N management strategies formulated in 2017/18 and 2018/19 wheat seasons were evaluated. In the atLeaf-based fertilizer N management experiment, prescriptive application of 40 kg N ha−1 at 10 days after seeding (DAS) and 60 kg N ha−1 at 30 DAS followed by application of an adjustable dose at Feekes 6 stage computed by multiplying the difference of atLeaf measurements of the test plot and the N-sufficient plot with 42.25 (as derived from the functional model developed in this study), resulted in grain yield similar or higher to that obtained by following the standard treatment. The LCC-based strategy to apply field-specific fertilizer N at Feekes 6 stage consisted of applying 150, 100 or 0 kg N ha−1 based on LCC shade equal to or less than 4, between 4 and 5 or equal to or more than 5, respectively. Both atLeaf- and LCC-based fertilizer N management strategies not only recorded the highest grain yield levels but also resulted in higher use efficiency with 57–60 kg N ha−1 in average less fertilizer use than the standard treatment.

S CREENING for stable genotype entails estimating the genotype (G)×environment (E) interaction (GEI) in multi-environmental trials (MET). Quinoa is a nutritionally rich crop as a source of vitamins, minerals and essential amino acids. It has been introduced to many countries in diverse regions worldwide. We evaluated five genotypes of quinoa under ten environments including irrigated and rain-fed conditions across Egypt. We used several stability parameters as well as additive main effects and multiplicative interaction (AMMI) analysis to determine the best genotype for each environment/location across Egypt. Based on AMMI analysis of variance, the sum of squares (SS) of E, G, and GEI explained ≈ 78%, 14%, 8%, respectively, of the treatment sum of squares. The SS of interaction principal components analysis axis1 (IPCA1) and IPCA2 explained 75 and 18%, respectively. KVL-SRA3 was the most stable genotype according to ecovalence value (W i), to deviation from regression coefficient value (S 2 d i) of Eberhart and Russell and to IPCA1, IPCA2 and AMMI stability value (ASV). Regalona was the most unstable genotype based on the same parameters. These results were visualized using AMMI biplot analysis, which revealed that KVL-SRA3 was widely adapted to all environments unlike Regalona that was poorly adapted to most environments. The Spearman's rank correlation among different stability parameters was significantly variable for both the five-quinoa genotypes and the ten investigated environments. Our results indicated that most stability parameters were consistent with AMMI parameters in identifying stable genotypes with some exceptions according to the concept of each of stability parameter (agronomic or biological). This study is an important step to open doors for the adoption of an extraordinary nutritional crop in Egypt. 5 Stability Parameters and AMMI Analysis of Quinoa (Chenopodium quinoa Willd.

In the North Western Coastal Zone (NWCZ) of Egypt, low rainfall results in poor crop production. Different techniques should be examined to enhance the crop yield productivity and increase the water use efficiency. The ridge-furrow water harvesting system (RFWHS) is examined under the rainfed conditions in the NWCZ of Egypt over the two growing seasons of 2012/2013 and 2013/2014. Two ridge:furrow ratios of 120:60 and 60:60 cm ridge:furrow were used and compared to the conventional cultivation in a flat plot. The RFWHS was combined with different plant densities produced from three different row spacing (i.e., 20, 30, 60 cm). The faba bean yield was highly influenced by the ridge:furrow ratio, the seed yield was increased by 47% and 128.2% when the 60:60 cm ridge:furrow ratio was used as compared to the conventional cultivation in the first and second seasons, respectively. The row spacing of 30 cm apart produced the highest seed yield of 491.1 kg/ha in the first season and 261.3 kg/ha in the second season as compared to 20 cm and 60 cm row spacing. The water use efficiency followed the same pattern as that of seed yield; it was the highest for the 60:60 cm ridge:furrow ratio and the highest for the 30 cm row spacing. It is concluded that the RFWHS can be used effectively in increasing faba bean production and maximizing water use efficiency in limited rainfall areas.ª 2015 Production and hosting by Elsevier B.V. on behalf of Faculty of Agriculture, Ain Shams University.