Gorgan University of Agricultural SciencesInternational Journal of Plant Production1735-68149320150701Photosynthetic parameter estimations by considering interactive effects of light, temperature and CO2 concentration321346222010.22069/ijpp.2015.2220ENL.P. GuoKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and
Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.H.J. KangKey Laboratories of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural
Resources Research, Chinese Academy of Sciences, Beijing 100101, China.Z. OuyangKey Laboratories of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural
Resources Research, Chinese Academy of Sciences, Beijing 100101, China.W. ZhuangKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and
Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.Q. YuKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and
Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.Journal Article20150509<span>Biochemical leaf photosynthesis models are evaluated by laboratory results and<br /><span>have been widely used at field scale for quantification of plant production,<br /><span>biochemical cycles and land surface processes. It is a key issue to search for<br /><span>appropriate model structure and parameterization, which determine model<br /><span>uncertainty. A leaf photosynthesis model that couples the Farquhar-von<br /><span>Caemmerer-Berry (FvCB) formulation to four different leaf temperature models is<br /><span>used to investigate the photosynthetic characteristics across a range of temperature<br /><span>gradients using both light (<span><em>A-Q</em><span>) and CO<span>2 <span>response curves (<span><em>A-C</em><span><em>i</em><span>). We used the<br /><span>Bayesian approach to fit the model to trial data of C<span>3 <span>crop plants (soybean, wheat)<br /><span>in the North China Plain and estimated key photosynthetic parameters, such as the<br /><span>maximum carboxylation rate of Rubisco (<span><em>V</em><span>cmax25<span>), the potential electron transport<br /><span>rate (<span><em>J</em><span>max25<span>), leaf dark respiration in the light (<span><em>R</em><span>d25<span>), mesophyll conductance (<span><em>g</em><span>m25<span>)<br /><span>and the kinetic parameter of Rubisco (<span><em>Г</em><span>*<span>25<span>) at a reference temperature of 25 °C.<br /><span>The results showed that 1) the model with moderate complexity showed the best<br /><span>goodness of fit, while conversely the simpler and more complex models were<br /><span>under and over fitting with their corresponding data, respectively; 2) the nonpeaked Arrhenius temperature response, which including both light and CO<span>2<br /><span>responses data gave the best estimates for the key parameters among the four<br /><span>models; and 3) the temperature gradient used to verify the model has greatly<br /><span>improved the estimation of six key parameters (<span><em>J</em><span>max25<span>, <span><em>V</em><span>cmax25<span>, <span><em>R</em><span>d25<span>, <span><em>Г</em><span>*<span>25<span>, <span><em>K</em><span>c25<span>, <span><em>g</em><span>m25<span>)<br /><span>with relatively more narrow confidence intervals (CIs) and showing regular <span>variation on temperature gradient. Overall, this method offers an accurate basis for<br /><span>estimating leaf photosynthesis parameters and may enhance the accuracy of<br /><span>canopy, ecosystem and even global vegetation models.</span></span><br /><br class="Apple-interchange-newline" /></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span>Gorgan University of Agricultural SciencesInternational Journal of Plant Production1735-68149320150701Impacts of projected changes and variability in climatic data on major food crops yields in Rwanda347372222110.22069/ijpp.2015.2221ENI. MuhireDepartment of Geography, Environmental Management and Energy Studies, University of Johannesburg,
Auckland Park, South Africa.F. AhmedSchool of Geography, Archaeology and Environmental Studies, University of Witwatersrand, Johannesburg,
South Africa.K. AbutalebDepartment of Geography, Environmental Management and Energy Studies, University of Johannesburg,
Auckland Park, South Africa.G. KaberaSouth African Medical Research Council, Biostatistics unit, Durban, South Africa.Journal Article20150509<span>This paper investigated the response of major food crop yields namely beans,<br /><span>cassava, Irish potatoes, maize and sweet potatoes to ongoing changes in climate in<br /><span>Rwanda. The projected daily precipitation and temperature data for the period<br /><span>2000-2050 used in this study were generated by stochastic weather generator<br /><span>(LARS-WG) from daily raw data for the period 1961 -2000. These data were<br /><span>collected from Rwandan Meteorological Center based in Kigali, while the<br /><span>agricultural records for the period 2000-2010 used to project yields of major food<br /><span>crops for 2011-2050 were obtained from the National Institute of Statistics of<br /><span>Rwanda and the Ministry of Agriculture and Animal Resources. A number of<br /><span>statistical techniques were applied in projecting the major food crops yields and<br /><span>attempting to quantify their magnitude trends in response to projected precipitation<br /><span>and temperature data. The climate and soil suitability analysis revealed that the<br /><span>central plateau and south-west regions of the country will be the most suitable<br /><span>regions for cultivation of major food crops except Irish potatoes which can be<br /><span>grown in the north-western highlands. The central plateau region is the only region<br /><span>that is expected to experience an increase in yields for most of the major food crops<br /><span>under investigation. The south-west region will have increased beans, cassava and<br /><span>sweet potatoes yields in season A (September-January). The eastern lowlands are<br /><span>expected to register a decreasing trend in most of crops yields in season A,<br /><span>corresponding to the anticipated decline in mean rainfalls and number of rainy<br /><span>days. The envisaged yields increase in season B (February-June) for beans, maize <span>and Irish potatoes will be in response to a rise in mean rainfall and number of rainy<br /><span>days. Heavy rainfall in the north-western region is likely to have a negative impact<br /><span>on crop yields. The rain might cause waterlogging, flooding events and landslides<br /><span>which may damage and destroy the crops.</span></span></span><br /><br class="Apple-interchange-newline" /></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span>Gorgan University of Agricultural SciencesInternational Journal of Plant Production1735-68149320150701Evaluation of the SALTMED model for sorghum under saline conditions in an arid region373392222210.22069/ijpp.2015.2222ENG.H. RanjbarDepartment of Crop Production and Plant Breeding, College of Agriculture, Shiraz University, Shiraz IranH. GhadiriDepartment of Crop Production and Plant Breeding, College of Agriculture, Shiraz University, Shiraz IranF. RazzaghiDepartment of Water Engineering, College of Agriculture, Shiraz University, Shiraz, Iran.A.R. SepaskhahDepartment of Water Engineering, College of Agriculture, Shiraz University, Shiraz, Iran.M. EdalatDepartment of Crop Production and Plant Breeding, College of Agriculture, Shiraz University, Shiraz IranJournal Article20150509<span>SALTMED model has been developed to predict yield, soil salinity and water<br /><span>content under saline conditions. A two year field experiment was carried out during<br /><span>2012-13 to calibrate and validate the model for sorghum. Plants were irrigated with<br /><span>salinity levels of 2, 6, 10 and 14 dS m<span>-1<span>. Results showed that there were significant<br /><span>differences between the observed and simulated sorghum dry matter (SDM) and<br /><span>yield. Absolute mean differences between the observed and simulated SDM values<br /><span>for 2, 6, 10 and 14 dS m<span>-1 <span>were 0.45, 1.53, 0.04 and 1.07 Mgha<span>-1<span>, respectively. Soil<br /><span>water contents (SWC) were overestimated at different soil depths. Mean<br /><span>differences between the simulated and observed SWC at 0.0-0.3, 0.3-0.6, 0.6-0.9<br /><span>and 0.0-0.9 m soil depths were 0.02, 0.04, 0.02 and 0.03 m<span>3<span>m<span>-3<span>, respectively. As<br /><span>salinity increased the mean differences between the observed and simulated SWC<br /><span>were increased. There were no significant differences between the observed and<br /><span>simulated soil salinities at 0.0-0.3, 0.3-0.6, 0.6-0.9 and 0.0-0.9 m soil depths. The<br /><span>Willmott index of agreement value of the observed and simulated EC<span>e <span>at different<br /><span>soil depth were between 0.92-0.96. It is concluded that following successful<br /><span>calibration, the SALTMED model could predict soil salinity and SWC with<br /><span>reasonably good accuracy at different water salinity levels. Although, SALTMED<br /><span>model reasonably well predicted soil salinity at different soil depth, there was a<br /><span>weak agreement between the observed and simulated soil water content at different<br /><span>soil depths. There was a fair agreement between the observed and simulated dry<br /><span>matter and grain yield at different water salinity levels.</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span></span></span></span></span></span></span></span></span></span></span></span></span>Gorgan University of Agricultural SciencesInternational Journal of Plant Production1735-68149320150701Seedling emergence response to temperature in safflower: measurements and modeling393412222310.22069/ijpp.2015.2223ENB. TorabiDepartment of Agronomy and Plant Breeding, Agriculture College, Vali-e-Asr University of Rafsanjan, Iran.M. AdibniyaDepartment of Agronomy and Plant Breeding, Agriculture College, Vali-e-Asr University of Rafsanjan, Iran.A. RahimiDepartment of Agronomy and Plant Breeding, Agriculture College, Vali-e-Asr University of Rafsanjan, IranJournal Article20150509<span>Quantitative information about the response of seedling emergence to<br /><span>temperature for safflower (<span><em>Carthamus tinctorius </em><span>L.) is rare. The main objective of<br /><span>the present study was to develop a model for predicting days to emergence for<br /><span>safflower as influenced by the temperature. In this regard, a field experiment with a<br /><span>range of sowing dates and four safflower cultivars were conducted to describe the<br /><span>response of seedling emergence to temperature and determine cardinal<br /><span>temperatures and biological days required for emergence (number of days to<br /><span>emergence under optimum temperatures). The segmented, dent-like and beta<br /><span>functions were used to describe the response of seedling emergence to temperature.<br /><span>Results showed that the segmented function described well the seedling emergence<br /><span>response to temperature with the cardinal temperatures of 3.4, 22 and 35 °C for<br /><span>base, optimum and ceiling temperatures, respectively. The biological days required<br /><span>for seedling emergence was estimated 8.6 days. Based on the findings, a seedling<br /><span>emergence model was conducted which can estimate time to 50% of emergence<br /><span>under variable temperature conditions. Model evaluation by using the some<br /><span>independent data showed that the model predicted time to 50% of emergence<br /><span>accurately (RMSD=1.3 days and R<span>2<span>=0.92).</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span></span></span></span></span>Gorgan University of Agricultural SciencesInternational Journal of Plant Production1735-68149320150701Meta-analysis of seed priming effects on seed germination, seedling emergence and crop yield: Iranian studies413432222410.22069/ijpp.2015.2224ENE. SoltaniDepartment of Agronomy and Plant Breeding Sciences, College of Abourahian, University of Tehran, Tehran, IranA. SoltaniAgronomy Group, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan 49138-15739, Iran.Journal Article20150509<span>A large body of research has documented the effect of seed priming on<br /><span>germination, emergence and crop yield. In such research, seed priming has been<br /><span>found to have a positive, negative or no effect. Meta-analysis can help to<br /><span>summarize and interpret a collection of experiments. The aim of this study was to<br /><span>conduct a meta-analysis to synthesize published data from studies addressing the<br /><span>effect of seed priming in Iran. Our results indicated that seed priming profoundly<br /><span>influences germination (rate or percentage), seedling emergence (rate or<br /><span>percentage) and crop yield. Among the studied traits, the crop yield increased the<br /><span>most (+28%), followed by the seedling emergence percentage (+19%), the<br /><span>germination rate (+17%), the seedling emergence rate (+15%) and the germination<br /><span>percentage (+4%). In general, hormonal priming was the best seed priming<br /><span>treatment. This was followed by hydropriming and osmopriming. The best priming<br /><span>durations were 12-24 h for the germination percentage (+14%), longer than 24 h<br /><span>for the germination rate (+16%), shorter than 24 h for the seedling emergence rate<br /><span>(+10 to + 14%) and the percentage (about +11%) and shorter than 12 h for the crop<br /><span>yield (+26%). Seed priming significantly increased in all of the traits of eudicots<br /><span>and monocots, except for the germination percentage in monocots. The differences<br /><span>were significant between the monocot and eudicot species in the germination stage.<br /><span>The differences became insignificant in the seedling emergence and crop yield.<br /><span>Finally, it was concluded that hydropriming is a practical treatment. This is due to<br /><span>its low cost and beneficial effects. We additionally concluded that durations shorter<br /><span>than 12 h are the most effective for this priming.</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span>Gorgan University of Agricultural SciencesInternational Journal of Plant Production1735-68149320150701Relationships between wheat yield, yield components and physico-chemical properties of soil under rain-fed conditions433466222510.22069/ijpp.2015.2225ENA. RanjbarIrrigation Department, Shiraz University, Shiraz, I.R. of IranA.R. SepaskhahIrrigation Department, Shiraz University, Shiraz, I.R. of IranS. EmadiIrrigation Department, Shiraz University, Shiraz, I.R. of Iran.Journal Article20150509<span>This research was conducted to study and classify the physico-chemical<br /><span>properties of soil, yield components of wheat and to determine the significance of<br /><span>these parameters on the grain yield formation. In this research, seven statistical<br /><span>methods consisting of simple correlation analysis (SCA), multiple linear regression<br /><span>(MLR), stepwise regression (SR), factor analysis (FA), principal component<br /><span>analysis (PCA), cluster analysis (CA) and path analysis (PA) have been<br /><span>investigated. The physico-chemical properties of soil, different morphological traits<br /><span>and wheat yield have been obtained from a field with 250×300 meter dimension<br /><span>located in Bajgah (with silty clay loam soil) that consisted of 30 samples. Among<br /><span>statistical analysis performed, MLR has provided more acceptable results. In this<br /><span>method, among the examined characteristics, five traits i.e., the number of stems<br /><span>without spikes per plant, biological yield, harvest index, soil soluble potassium and<br /><span>soil available phosphorus, examined 98.3% of the variations of the yield (P<0.05).<br /><span>Lack of soil nitrogen effect on yield is due to drought stress conditions in which the<br /><span>plant growth is less sensitive to nitrogen. Furthermore, the negative effect of<br /><span>phosphorus on the yield of plant may be due to the inverse relationship between the<br /><span>soil phosphorus and micronutrient elements on the plant growth. Generally, among<br /><span>the yield components, biological yield is the most important and effective trait on<br /><span>grain yield, that has presented a significant contribution in different statistical<br /><span>methods. For some of the used statistical methods, the measured traits, like length<br /><span>of spike, the number of spikes per square meter, the number of grains per spike<br /><span>and harvest index showed positive effects on the grain yield and other traits like</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /></span><span>1000-grains weight, the weight of grain per spike and the number of tillers without<br /><span>spikes per plant showed negative effects on the grain yield with the highest<br /><span>correlation. Among different soil nutrition, soluble potassium, phosphorus, sulfate<br /><span>and available potassium with positive effects and the clay content with negative<br /><span>effects showed the most correlation with the grain yield.</span></span></span></span><br /><br class="Apple-interchange-newline" /></span>Gorgan University of Agricultural SciencesInternational Journal of Plant Production1735-68149320150701Improving barley performance by proper foliar applied salicylic-acid under saline conditions467486222610.22069/ijpp.2015.2226ENH. Pirasteh-AnoshehDepartment of Crop Production and Plant Breeding, College of Agriculture, Shiraz University, Shiraz IranY. EmamDepartment of Crop Production and Plant Breeding, College of Agriculture, Shiraz University, Shiraz IranA.R. SepaskhahDepartment of Water Engineering, College of Agriculture, Shiraz University, Shiraz, Iran.Journal Article20150509<span>Despite general effect of salicylic-acid (SA) in improving plant growth and<br /><span>productivity in saline conditions, there have not been unanimity about the best<br /><span>concentration. In this 2-yr field study the effect of different SA concentrations<br /><span>(0, 0.5, 1.0, 1.5 and 2.0 mM) was examined on growth, grain yield and yield<br /><span>components of barley under two non-saline (2 dS m<span>-1<span>) and saline (12 dS m<span>-1<span>)<br /><span>conditions. By using response curves and regression analyses the best concentration<br /><span>was also determined. The results showed that salt stress decreased barley plant<br /><span>height (22.7%), fertile tillers (19.0%), ear length (21.6%), grain number per ear<br /><span>(22.5%), thousand grain weight (19.9%), biological yield (29.6%) and grain yield<br /><span>(37.6%). Since salinity treatment when imposed the tillers were at their rapid<br /><span>growth phase; therefore, fertile tiller number per unit area was found to be the most<br /><span>sensitive trait to salt stress. Nonetheless, SA foliar application in different<br /><span>concentrations could ameliorate some of these negative impacts on growth, yield<br /><span>and yield components. Reduction percentage of grain yield due to salinity was<br /><span>the lowest at 1.5 mM in first and 1.0 mM SA concentration in second year<br /><span>corresponding to 27.3% and 33.8%, respectively; while those were highest at no-SA<br /><span>treatments (42.2% and 43.8% in first and second year, respectively). Modulating role<br /><span>of SA for adverse effect of salinity could be attributed to enhanced grain number.<br /><span>Based on the result of regression analysis, it can be concluded that SA foliar<br /><span>application at 2.0 mM under non-saline and at 1.41 mM under saline conditions<br /><span>could be considered as the best concentrations for improving barley performance.</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span></span></span></span></span>Gorgan University of Agricultural SciencesInternational Journal of Plant Production1735-68149320150701Structure of weed communities occurring in crop rotation and monoculture of cereals487506222710.22069/ijpp.2015.2227ENA. WoźniakDepartment of Herbology and Plant Cultivation Techniques, University of Life Sciences in Lublin, Poland.M. SorokaDepartment of Botany, Ukrainian National Forestry University, 79057 Lviv, Ukraine.Journal Article20150509<span>A strict field experiment with crops sown in crop rotation and monoculture was<br /><span>carried out in the years 1988-2012 at the Experimental Station Uhrusk belonging to<br /><span>the University of Life Sciences in Lublin, south-eastern Poland. The study was<br /><span>aimed at evaluating the structure of weed communities occurring in crop rotation<br /><span>and monoculture of cereals. The highest weed density m<span>-2 <span>was determined in<br /><span>the second (1992-1996) and the third crop rotation (1997-2000), whereas the<br /><span>highest weight of weeds was noted in the third crop rotation. Weed community in<br /><span>cereals sown in crop rotation and monoculture was constituted by species<br /><span>belonging to 6 syntaxonomic classes: <span><em>Stellarietea mediae</em><span>, <span><em>Artemisietea vulgaris</em><span>,<br /><span><em>Molinio-Arrhenatheretea</em><span>, <span><em>Agropyretea intermedio-repentis</em><span>, <span><em>Koelerio glaucaeCorynephoretea canescentis </em><span>and <span><em>Bidentetea tripartiti</em><span>.</span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span></span></span></span></span></span></span></span></span></span></span></span></span></span>