Understanding physical clogging in drip irrigation: in situ,in-lab and numerical approaches

Dripper clogging is a major drawback of microirrigation systems that must be addressed to improve their efficiency and durability. Particle-induced clogging is first studied in situ. The experiments consist in observing in real conditions the behavior of a series of drippers fitted on an agricultural plot in the south of France. The plot is supplied from a canal with Durance River water. The latter is loaded with sediments that gradually clog drippers and filters. Water analysis reveal that physicochemical clogging prevails over biological clogging. This characterization helps in setting in-lab experiment protocol. Indeed, besides field observation of clogging, laboratory analyses of both the irrigation water and the clogging material are performed with reactive and inert clay: smectite and an illite–calcite mix. A surprising tendency is observed: Salt concentration in smectite seeded water decreases the clogging, whereas it increases agglomerate size. Computational fluid dynamic simulations are carried out to investigate the impact of particles on flow behavior. Results demonstrate that clay particles interacting with the flow govern the complex structure of the fluid velocity fields inside the dripper labyrinth channel.     

“WaterMan”: an on-farm water management game with heuristic capabilities

A modern computer-based simulation tool (WaterMan) in the form of a game for on-farm water management was developed for application in training events for farmers, students, and irrigators. The WaterMan game utilizes an interactive framework, thereby allowing the user to develop scenarios and test alternatives in a convenient, risk-free environment. It includes a comprehensive soil water and salt balance calculation algorithm. It also employs heuristic capabilities for modeling all of the important aspects of on-farm water management, and to provide quantitative performance evaluations and practical water management advice to the trainees. Random events (both favorable and unfavorable) and different strategic decisions are included in the game for more realism and to provide an appropriate level of challenge according to player performance. Thus, the ability to anticipate the player skill level, and to reply with random events appropriate to the anticipated level, is provided by the heuristic capabilities used in the software. These heuristic features were developed based on a combination of two artificial intelligence approaches: (1) a pattern recognition approach and (2) reinforcement learning based on a Markov decision processes approach, specifically the Q-learning method. These two approaches were combined in a new way to account for the difference in the effect of actions taken by the player and action taken by the system in the game world. The reward function for the Q-learning method was modified to reflect the suggested classification of the WaterMan game as what is referred to as a partially competitive and partially cooperative game.     

Yield response and <Emphasis Type="Italic">Stenocarpella</Emphasis> ear rot reaction among selected maize inbred lines and top cross hybrids

Stenocarpella ear rot of maize caused by Stenocarpella maydis (Berk.) Sutton is one of the most important ear rot diseases in South Africa. The objective of this study was to identify suitable maize inbred lines and top cross hybrids with Stenocarpella ear rot resistance and high grain yield. The study was conducted using 54 inbred lines and 54 top crosses with selected standard checks under natural and artificial disease inoculations. A split-plot design was used with three replications. There were considerable variations among tested inbred lines and top crosses for Stenocarpella ear rot resistance and grain yield. Two promising inbred lines GCI-35 and GCI-50 were selected possessing resistance to Stenocarpella ear rot and increased grain yield. Inbred line GCI-35 had a grain yield of 6.31 and 6.27 t/ha under natural and artificial inoculations, respectively. Line GCI-50 yielded 5.8 and 5.7 t/ha when tested under natural and artificial disease infestations, respectively. Two top cross hybrids GCT-14 and GCT-30 were selected with greater grain yield and high level of Stenocarpella ear rot resistance. Top cross entry GCT-14 yielded 7.5 and 9.33 t/ha, respectively, whereas GCT-30 had yield of 7.65 and 9.21 t/ha, respectively. The selected inbred lines and top cross hybrids will be useful in maize improvement for Stenocarpella ear rot resistance and increased grain yield in South Africa or other similar environments.     

Assessment of genetic divergence in salt tolerance of soybean (<Emphasis Type="Italic">Glycine max</Emphasis> L.) genotypes

A large number of soybean (Glycine max L.) genotypes of diverse growth habit and adaptive characters were used in the experiment. Soil salinity-induced changes in nine morpho-physiological characters of 30-day-old seedlings of 170 soybean genotypes were compared in the study. The first and second principal components (PC) of principal component analysis (PCA) results accounted for 97 and 2.5%, respectively, of the total variations of soybean genotypes. The variation for the first PC was composed mainly of relative total dry weight (DW), relative shoot dry weight, as well as petiole dry weight. There were four clusters distinguished in the cluster analysis. The genotypes in cluster IV performed better in respect to relative total dry weight and relative shoot dry weight and hence having salt tolerance. The genotypes clusters III performed very poorly and those of clusters II and I were moderate to poor. D2 analysis indicated that the clusters differed significantly from each other. Discriminant function analysis (DFA) again asserts strongly that more than 92% of the genotypes were correctly assigned to clusters. Both PCA and DFA confirmed that the relative total DW followed by shoot and petiole DW were the major discriminatory variables, and the root DW were the secondary important variables to distinguish genotypes into groups. In this study, multivariate analyses were used in identifying the soybean genotypes of desirable traits for salt tolerance.     

Identifying high yielding stable winter wheat genotypes for irrigated environments in Central and West Asia

Improved winter wheat (Triticum aestivum L.) cultivars are needed for the diverse environments in Central and West Asia to improve rural livelihoods. This study was conducted to determine the performance of elite winter wheat breeding lines developed by the International Winter Wheat Improvement Program (IWWIP), to analyze their stability across diverse environments, and to identify superior genotypes that could be valuable for winter wheat improvement or varietal release. One hundred and one advanced winter wheat breeding lines and four check cultivars were tested over a 5-year period (2004–2008). Grain yield and agronomic traits were analyzed. Stability and genotypic superiority for grain yield were determined using genotype and genotype × environment (GGE) biplot analysis. The experimental genotypes showed high levels of grain yield in each year, with mean values ranging from 3.9 to 6.7 t ha⁻¹. A set of 25 experimental genotypes was identified. These were either equal or superior to the best check based on their high mean yield and stability across environments as assessed by the GGE biplot analysis. The more stable high yielding genotypes were ID800994.W/Falke, Agri/Nac//Attila, ID800994W/Vee//F900K/3/Pony/Opata, AU//YT542/N10B/3/II8260/4/JI/Hys/5/Yunnat Esskiy/6/KS82W409/Spn and F130-L-1-12/MV12. The superior genotypes also had acceptable maturity, plant height and 1,000-kernel weight. Among the superior lines, Agri/Nac//Attila and Shark/F4105W2.1 have already been proposed for release in Kyrgyzstan and Georgia, respectively. The findings provide information on wide adaptation of the internationally important winter wheat genotypes, and demonstrate that the IWWIP program is enriching the germplasm base in the region with superior winter wheat genotypes to the benefit of national and international winter wheat improvement programs.     

Crop response of drought-tolerant and conventional maize hybrids in a semiarid environment

In the High Plains, corn (Zea mays L.) is an important commodity for livestock feed. However, limited water resources and drought conditions continue to hinder corn production. Drought-tolerant (DT) corn hybrids could help maintain high yields under water-limited conditions, though consistent response of such hybrids is unverified. In this two-year study, the effects of three irrigation treatments were investigated for a DT and conventional maize hybrid, Pioneer AQUAMax P0876HR and Pioneer 33Y75, respectively. In 2013, the drier of the 2 years, irrigation amounts and crop water use (ET_c) were greater for the conventional hybrid, but grain water use efficiency (WUE) and harvest index were significantly greater for the DT hybrid. In 2014, grain yields and WUE were not significantly different between hybrids. However, irrigation amounts, ET_c and biomass yields were greater for the conventional hybrid. Results from both years indicate that the DT hybrid required less water to maximize grain yield as compared to the conventional hybrid. Producing relatively high yields with reduced amounts of water may provide a means for producers to continue corn production in a semiarid environment with declining water supplies.     

Measured and estimated water use and crop coefficients of grapevines trained to overhead trellis systems in California’s San Joaquin Valley

The use of overhead trellis systems for the production of dry-on-vine (DOV) raisins and table grapes in California is expanding. Studies were conducted from 2006 to 2009 using Thompson Seedless grapevines grown in a weighing lysimeter trained to an overhead arbor trellis and farmed as DOV raisins for the first two years and for use as table grapes thereafter. Maximum canopy coverage for the two lysimeter vines across years was in excess of 80 %. Seasonal (15 March–31 October) evapotranspiration for the lysimeter vines (ET_Lys) was 952 mm in 2007 (farmed as DOV raisins) and 943 and 952 mm (when farmed as table grapes). The maximum crop coefficient (K _cLys) across all 4 years ranged from 1.3 to 1.4. These maximum values were similar to those estimated using the relationship where K _c is a function of the amount of shaded area measured beneath the canopy at solar noon (K _c = 0.017 × percent shaded area). Covering the lysimeter’s soil surface with plastic (and then removing it) numerous times during the 2009 growing season (1 June–14 September) reduced ET_Lys from an average of 6.4 to 5.6 mm day^?1 and the K _c from 1.07 to 0.93. A seasonal basal K _c (K _cb) was calculated for grapevines using an overhead trellis system with a 13 % reduction in the K _cLys across the growing season.     

Genetic variation and environmental stability of grain mineral nutrient concentrations in <Emphasis Type="Italic">Triticum dicoccoides</Emphasis> under five environments

Nineteen wild emmer wheat [Triticum turgidum ssp. dicoccoides (Körn.) Thell.] genotypes were evaluated for the grain concentrations of phosphorous (P), potassium (K), sulfur (S), magnesium (Mg), calcium (Ca), zinc (Zn), manganese (Mn), iron (Fe) and cooper (Cu) under five different environments in Turkey and Israel. Each mineral nutrient has been investigated for the (1) genotype by environment (G × E) interactions, (2) genotype stability, (3) correlation among minerals and (4) mineral stability. Among the macronutrients analyzed, grain concentrations of Ca (range 338–2,034 mg kg^?1) and S (range 0.18–0.43%) showed the largest variation. In the case of micronutrients, the largest variation was observed in the grain Mn concentration (range 13–87 mg kg^?1). Grain concentrations of Fe and Zn also showed important variation (range 27–86 and 39–115 mg kg^?1, respectively). Accessions with higher nutrient concentrations (especially Zn and Fe) had also greater grain weight, suggesting that higher grain Zn and Fe concentrations are not necessarily related to small grain size or weight. Analysis of variance showed that environment was the most important source of variation for K, S, Ca, Fe, Mn and Zn, explaining between 44 and 78% of the total variation and G × E explained between 20 and 40% of the total variation in all the minerals, except for S and Zn where its effect accounted for less than 16%. Genotype was the most important source of variation for Cu (explaining 38% of the total variation). However, genotype effect was also important for Mg, Mn, Zn and S. Sulfur and Zn showed the largest heritability values (77 and 72%, respectively). Iron exhibited low heritability and high ratio value between the G × E and genotype variance components \( \left( {\sigma_{\text{GE}}^{2} /\sigma_{G}^{2} } \right) \), suggesting that specific adaptation for this mineral could be positively exploited. The wild emmer germplasm tested in the current study revealed some outstanding accessions (such as MM 5/4 and 24-39) in terms of grain Zn and Fe concentrations and environmental stability that can be used as potential donors to enhance grain micronutrient concentrations in wheats.     

Mapping of quantitative trait loci controlling powdery mildew resistance in Mungbean (<Emphasis Type="Italic">Vigna radiata</Emphasis> (L.) Wilczek)

Powdery mildew disease in mungbean is caused by the fungus, Erysiphe polygoni D.C. We identified two quantitative trait loci (QTLs) controlling resistance to the disease in a RIL population of 190 F₇ lines. The population was developed from the cross between a susceptible cultivar, “Kamphaeng Saen 1” and a resistant line, “VC6468-11-1A”. Reaction to the disease was evaluated for resistance in field and greenhouse conditions. Results from analysis of variance revealed that 15 SSR loci on three linkage groups (LG) associated with the resistance. Composite interval mapping consistently identified two QTLs on two LGs, qPMR-1 and qPMR-2, conferring the resistance. qPMR-1 and qPMR-2 accounted for 20.10 and 57.81% of the total variation for plant response to the disease, respectively. Comparison based on common markers used in our and previous studies suggested that qPMR-2 is possibly the same as the major QTL reported earlier using another resistant source. The SSR markers flanking and closely linked to qPMR-1 (CEDG282 and CEDG191) and qPMR-2 (MB-SSR238 and CEDG166) are useful for marker-assisted selection for mungbean resistance to powdery mildew.