International Journal of Plant Production
Volume:13 Issue: 1, Mar 2019

Rice phenology was determined by temperature and agronomic management. It was essential to quantify the interaction between warming temperatures and rice phenology to understand the impact of climate change in rice farming systems. Based on the quantitative analysis of data from double-cropping rice in the south of China in the last 20 years, we found that the average temperature of the whole growth periods of early rice and late rice increased by an average of 0.76 °C and 0.49 °C decade−1, respectively. The whole growth period (WGP) change trend of early rice decreased by 0.07 d decade−1, but the WGP of late rice increased 1.26 d decade−1. The vegetative growth period (VGP) and the reproductive growth period (RGP) of early rice and late rice were all shorted. Regression analysis showed that the VGP of early rice and late rice decreased by 2.99 days and 2.82 days, and the RGP decreased by 1.67 days and 1.81 days, respectively, as the mean air temperature increased by 1 °C during the corresponding stages. The WGPs of early rice and late rice decreased by 3.81 days and 5.31 days, respectively, with the mean air temperature increased by 1 °C during growth duration. According to our studies, the rising mean air temperature could lead to a shortening of the double-rice growth period. Therefore, we must adjust the planting pattern of the double-rice system to mitigate and adapt to future climate change.

Data mining approaches are designed for classification problems in which each observation is a member of one and only one class. In this study, a non-deterministic approach based on C5.0 data mining algorithm has been employed for discriminating the phenological stages of maize from emergence to dough, in a field located in Karaj, Iran. Two readily-available predictors i.e. accumulated growing degree days (AGDD) and multi-temporal LANDSAT7-extracted normalized difference vegetation index (NDVI) was used to build the decision tree. The AGDD was calculated based on three cardinal thresholds of temperature i.e. effective minimum, optimum, effective maximum. The NDVI was compared with two recently developed indices namely, enhanced vegetation index2 (EVI2) and optimized soil adjusted vegetation index (OSAVI) using the signal to noise ratio (SNR) criterion. Findings confirmed that these three remotely sensed indices do not have significant differences, therefore, the smoothed time series of NDVI was used in the C5.0 algorithm. The precisions of classification by C5.0 data mining algorithm in partitioning of training and testing data were approximately 90.51 and 81.77%, respectively. The mean absolute error (MAE) values of the onset of maize phenological stages were estimated about 2.6–5.3 days for various stages by C5.0 model. While corresponding values for the classical AGDD model were 3.9–10.7 days. This confirms the skill of data mining approach in comparison with commonly-used the classical AGDD model in applications of real time monitoring.

To evaluate the effects of planting methods and deficit irrigation, an experiment was conducted on treatments of direct seeding with full irrigation (DSI1), transplanting on Nov. 1 with full irrigation (T1I2), direct seeding with omitting one irrigation at rosette stage (DSI3), direct seeding with omitting two irrigations at rosette stage (DSI4), direct seeding with omitting irrigation after rosette and before flowering stage (DSI5), direct seeding with omitting irrigation from the grain filling stage to physiological maturity (DSI6) and transplanting on Oct. 9 with full irrigation (T2I7) on yield, water productivity and actual crop evapotranspiration of canola cultivars (Neptune and Danube), at the research station of School of Agriculture, Shiraz University, Shiraz, Islamic Republic of Iran, in the two consecutive growing seasons (2015–2017). This study was carried out as a factorial experiment based on complete randomized block design with four replicates. In this study the AquaCrop model was used for estimating root depth and actual crop evapotranspiration. The results showed that planting methods had significant effect on the grain and oil yields. It was also revealed transplanting on Nov. 1 was not a suitable date for transplanting. Water productivity for grain and oil yields was calculated based on irrigation water and actual crop evapotranspiration. The lowest water productivity was obtained 0.03 kg m−3 based on actual crop evapotranspiration in the second year for oil yield in the T1I2 treatment. Also, the results indicated that in the direct seeding treatments DSI6 treatment and in cultivars, Neptune had the lowest grain and oil yields.

Increasing species diversity through intercropping can be effective in improving the ecosystem functions and services. In this experiment, three different functional groups, soybean (Glycine max) as a C3 and fixing nitrogen plant, corn (Zea mays) as a C4 and nitrogen-demanding crop and marshmallow (Althaea officinalis) as a perennial medicinal plant, were cultivated as double and triple intercropping. The purpose of this study was to evaluate different patterns of intercropping in terms of production enhancement, weeds control, increase of nutrients uptake and improvement of soil biological properties as different ecosystem services and compare them with the sole cropping systems. For this purpose, two experiments were conducted based on strip design as a randomized complete block with three replications at research farm of Ferdowsi University of Mashhad in 2014 and 2015. Experimental treatments (14 treatments) were weeding and non-weeding, different planting patterns as double intercropping (corn + soybean, corn + marshmallow and soybean + marshmallow), triple intercropping (corn + soybean + marshmallow) and also their sole cropping. The results indicated that the highest density of weeds was observed by sole cropping of soybean among the different planting patterns in both years (46.7 plants per m2 in the first year and 47.3 plants per m2 in the second year) and the minimum number of weeds was observed under intercropping of corn + soybean + marshmallow with 37.6 plants per m2 for the first year and sole cropping of Marshmallow by 13 plants per m2 for the second year. Planting pattern treatment had no significant effect on Shannon–Weaver diversity index; however, the findings of the study illustrated that increasing plant diversity as intercropping caused to increase in weeds diversity. Among different intercropping patterns, the highest amount of nitrogen uptake was achieved from corn + soybean intercropping. In both weeding and non-weeding, phosphorous uptake in corn monoculture was higher than soybean and marshmallow monoculture, so that in the weed absence, phosphorus absorption in corn, soybean and marshmallow monocultures were 85.50, 46.66 and 44.51 kg ha−1 respectively. Using the intercropping systems increased the soil microbial respiration and biomass in comparison with sole cropping systems. According to the findings of this study, it can be mentioned that intercropping systems not only increase economic yield but also improve other ecosystem services.

Understanding how crop phenology responds to historical climate change is a prerequisite for evaluating crop phenology and future yield responses. Most phenology response investigations are based on the phenology observed under circumstances of varieties changing over time, which then necessitates disentangling the role of climate change from the effect of changing varieties using various models. However, results from such studies are limited by the uncertainties caused by model mechanisms and assumptions and parameter calibration and validation. In this study, phenology observations were made for varieties of winter wheat (Triticum aestivum L.), rice (Oryza sativa L.), and spring maize (Zea mays L.) at 11 agro-meteorological observation sites in north China. The varieties observed for each species did not change over a period of at least 15 years. The observations were used to investigate the measured phenology response to climate. Dates of major wheat phenology stages tended to occur earlier due to warming, but the trend in rice and spring maize was not clear. Growth duration was shortened during the vegetative period of winter wheat, but was prolonged during vegetative period of rice and in the reproductive period of winter wheat and rice. Growing degree days (GDD) were generally increased for both vegetative and reproductive periods for all crops except during the vegetative period for winter wheat. We found that most trends in date of phenology stages, duration of growth phases, and GDD were similar to previous reports in which the varieties observed did not remain constant. This indicates that previous reports are likely to have overestimated the effect of cultivar shifting on crop phenology and underestimated the role of climate. Based on our results, growth duration under future warmer conditions may be longer than previously simulated, and hence yield may also be higher than previously estimated.

Growth, yield components, and N utilization patterns of spring chickpea (Cicer arietinum L.) cultivars were investigated for 2 years under Mediterranean conditions, and the associations with seed yield were identified. Three desi-type cultivars (Andros, Kassos, and Serifos) and one kabuli-type cultivar (Zehavit-27) were assessed under March and April planting. April planting resulted in higher early dry matter and N accumulation, which were not reflected in increased seed yield compared with March planting. Chickpea growth was co-limited by both source and sink limitations induced by the environment during the seed filling period. Most of the variation (> 94%) in seed yield was accounted for by the variation in seeds m−2 rather than that in seed size. The seed number was reduced on average by 7–18.2 seeds m−2 day−1 of planting delay, with the penalty being higher the year with the greater yield potential. In addition, the number of seeds m−2 was reduced by 21% and by 18% for every degree Celsius increase in mean and maximum temperature during the early reproductive period, respectively. Biomass production efficiency (33.9–51.6 kg kg−1) was greater than nitrogen utilization efficiency (17.8–27.2 kg kg−1). The proportion of total plant N allocated to the seeds at maturity (i.e., nitrogen harvest index) was the limiting factor for enhanced N utilization. Overall, March planting may ameliorate growth limitations by bringing the reproductive stage earlier and, consequently, it may be more advantageous in terms of both seed yield and seed quality compared with April planting.

Limited studies have been conducted to assess key yield-determining processes and their inter-relationships in okra under both nitrogen (N) poor as well as optimum growing environments on a set of contrasting genotypes. Therefore, in this study, we analyze the yield formation in two diverse genotypes of okra (Sabz Pari ‘SP’ and Arka Anamika ‘AA’) across seven contrasting N environments (0–300 kg ha−1) and to determine effects of genetic (G), environmental (E) and their interaction (G × E) on a set of traits relevant to vegetative growth, onset of reproductive cycle, pod characteristics, and yield. The effects of G, E, and G × E were significant for majority of traits. Ranking of genotypes changed across the environments for most of the traits indicating a cross-over type G × E interactions. Majority of traits showed differential response to varied N availability. Mean values of plant height, canopy diameter, leaves plant−1, leaf area plant−1, stem girth, internodes plant−1, length of internode, pod length, pod diameter, and pod yield enhanced with rising N (50–300 kg ha−1). However, pod fresh weight and number of pods plant−1 declined with N availability above 150 kg ha−1. The onset of first flowering delayed with decline in N availability and vice versa. Late maturing genotype AA indicated a better yield potential across N environments by producing 50.96% more yield over early maturing genotype SP. There existed significant inter-relationships among most of the traits. Four traits including leaf area plant−1, pod diameter, pod fresh weight and number of pods plant−1 explained most of the variance (97.4%) in pod yield. It was concluded that gain in these parameters may lead to an increase in pod yield. Our approach could be useful in developing an indirect selection criterion for yield improvement in okra and could provide a theoretical framework for breeding programmes in developing an ideal genotype.