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Plants subjected to intermittent water management (AWD) and late N fertilisation (N8L) had 15 and 35% lower ratios of tiller emergence and tiller survival, respectively. No response to N fertilisation was observed in plants grown under SWM. Overall, the impact of AWD-N8L resulted in a lower panicle number per plant and the subsequent decline in plant yield (Fig. 1B), although not significantly likely due to the variability observed. The reduced panicle number and yield of the AWD-N8L treatment could be a response to a N shortage caused by the added effect of 1) a loss of N through denitrification due to the watering system (Sah et al., 1083) and 2) the lack of N supply in early plant development (Pham Quang et al., 2004). However, our results indicated that these negative effects could be overcome by improving the precision of N through earlier N fertilisation.
By doing so, a good adjustment between plant N demand and N availability could be achieved leading to an improvement in the fertilisation efficiency under AWD irrigation system.
4. Conclusion Nitrogen fertilisation at the beginning of the tillering stage increased tiller emergence and promoted the development of lower nodes. There was a trend for yield to increase under these conditions. In addition, there was a significant interaction between timing of N fertilisation and the irrigation system; the intermittent water management and N fertilisation at mid-late tillering reduced the emergence and survival of tillers resulting in a decline in plant yield. This performance should be considered for the use of water saving technologies.
References Gendua, P.A., Yamamoto, Y., Miyazaki, A., Yoshida, T. and Wang, Y.L., 2009. Effects of the Tillering Nodes on the Main Stem of a Chinese Large-Panicle-Type Rice Cultivar, Yangdao 4, on the Growth and Yield-Related Characteristics in Relation to Cropping Season. Plant Production Science 12, 257-266.
IPCC, 2001. Working Group II: Impacts, Adaptation and Vulnerability – Contribution of the Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change.
Pham Quang, D., Mitsugu, H., Satoru, S. and Eiki, K., 2004. Varietal differences in tillering and yield responses of rice plants to nitrogen-free basal dressing accompanied with sparse planting density in the Tohoku region of Japa. Plant Production Science 7, 3-10.
Sah, R, Mikkelsen, D., 1983. Availability and utilization of fertilizer nitrogen by rice under alternate flooding. Plant Soil, 75, 227-234 Zeng, L.H., Lesch, S.M. and Grieve, C.M., 2003. Rice growth and yield respond to changes in water depth and salinity stress. Agricultural Water Management 59, 67-75.
Nitrogen Workshop 2012
Improvement of sensor based N application approach in winter wheat by incorporation of soil and terrain properties Samborski, S.a, Dobers, E.S.b, Gozdowski D.c, Stępień M.a a Department of Agronomy, Warsaw University of Life Sciences, Poland b Ag-GeoData, Göttingen, Germany c Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences, Poland
1. Background & Objectives Optical sensors estimate different crop properties among them N status and biomass. Therefore, sensor based nitrogen (N) applications have been used in different crops, including winter wheat, to optimize variable N fertilization on fields with varying growing conditions. N rate applied at the beginning of the booting stage is very important for grain yield formation of winter wheat. But at this growth stage plants may not show yet their yield potential expressed as a vegetation index (VI) for different sites, due to the lack of water deficit symptoms at this time. As a consequence an optical sensor will be able to discriminate canopy variability in situ but it may not be able to make appropriate corrections for lower yield potential in less productive parts of the field, which will appear later in time. To overcome these limitations the use of a map overlay approach for more precise N fertilization seems to be relevant. Integration of information on soil quality (Holland and Schepers, 2010) and field topography (Soil Fertility Manual, 2006) may improve N fertilization algorithms. The aim of this study was to propose for future testing an approach for reduced use of N fertilisers in less responsive zones reasoned by suboptimal conditions for N uptake (Nupt).
2. Materials & Methods The research was conducted in the season 2009/2010 in northern Poland (54° 31' N 17° 18' E) on a field cropped with winter wheat (Triticum aestivum L.,) and farmed by Farm Frites Poland Dwa Sp.
z o.o. The field under study (ca. 30ha) is dominated by Dystric Cambisols (WRB, 2006) with predominantly sandy loam texture developed from glacial moraine deposits of the last glaciation.
As 1st N application at growth stage (GS) 22 (Zadoks et al., 1974) the field received a uniform N rate of 80 kg ha-1 as urea ammonium nitrate solution (UAN) with 32% N. For the 2nd (GS 32) N application, the field was divided into tramline wide strips, fertilized alternatively with a variable or uniform N dose of ammonium nitrate (34%). For variable N application two Crop CircleTM ACSsensors (Holland Scientific, Lincoln, NE) were used. Reflection data was used for the calculation of the Green Normalized Difference Vegetation Index (GNDVI, Dellinger et al., 2008).
Data on the relationship GNDVI and Green Area Index (GAI), N uptake per unit GAI, established on a N-response experiment adjacent to the study field were used to calculate variable N rate.
Grain yield was measured at harvest time by yield monitors with DGPS data logging. Nitrogen surplus or deficit for the whole research field was calculated as the difference between total N applied and total N uptake, estimated from yield data. The relationship between grain yield vs. N uptake was determined for whole plant samples collected over the entire area of the research field.
Agricultural soil map information was used to derive the data on the variability of the soil’s potential productivity (SPP). This map covered three agricultural suitability complexes (ASC) numbered 2, 4 and 5 with the SPP respectively of 100%; 82.7% and 65.4%. The SPP calculation was based on the same relation of winter wheat grain yields obtained on the same complexes in field experiments carried out in Poland in the 70’ and 80’ of the 20th century (Woch et al., 2006).
Elevation data from tractor mounted RTK-dGPS measurements registered during tillage operations, were used for the calculation of terrain slope. In previous analyses we found that an increase in slope of 1 degree reduced the total N uptake by 4.75 kg ha-1 Therefore, similar reduction in total N Nitrogen Workshop 2012 applied, but corrected by the share of the 2nd dressing in total N applied of 36% was proposed. After this correction the reduction in N rate per 1 degree of slope rise was 1.71 kg N ha-1.
3. Results & Discussion The southern part of the field showed the highest N surplus independently from the N application strategy (Figure 1a). This suggests that the cause of N surplus has to be related to some other factors, not yet incorporated in the map overlay and a sensor based N application algorithm. Soil map data (Figure 1a) and slope maps superimposed on the N surplus/deficit areas indicate that the southern part of the field is characterized by coarser soil (ASC 4 and 5) and steeper, eroded slopes (data not shown). This resulted in lower SPP thus decreased N uptake, not exceeding the amount of total N applied and consequently higher N surplus. The use of a sensor alone at GS 32 for VRA does not allow to reduce N rate in areas with lower SPP (Figure 1b). This is because the default strategy for control of N rate by crop reflectance is to apply more N on poor areas to maintain tillers and grain numbers. In our simulation study to limit N oversupply on less productive southern part of the field the information provided by the soil and slope maps has been incorporated in the calculation of the variable N rate. Figure 1c presents the map of N applied where the information from the sensor, soil and terrain slope maps has been combined. Incorporation of soil map and topography data would help to reduce average variable N rate used at GS32 respectively by 2.5 (range 0-23.5) and 2,4 (range 0-20.5), kg ha-1 in comparison to the average amount of N applied when only sensor information was used.
Figure 1. Maps of: a) N surplus or deficit, b) N application sensor based, c) N application sensor, soil and slope based.
4. Conclusions This novel approach of incorporating soil and terrain properties into calculation of variable N rate improves the sensor based N application alone by applying less N in the potentially less responsive zones. The next step in the improvement of the N application algorithm should be testing if reduced use of N fertilisers, based on the soil map and topography data in the potentially less responsive zones, do not cause significant yield decrease.
References Dellinger, A.E., Schmidt, J.P. and Beegle, D.B. 2008. Developing Nitrogen Fertilizer Recommendations for Corn Using an Active Sensor. Agronomy Journal 100, 1546-1552.
Holland, K.H. and Schepers, J.S. 2010. Derivation of a Variable Rate Nitrogen Application Model for In-Season fertilization of Corn. Agronomy Journal 102, 1415-1424.
WRB. 2006. World Reference Base for Soil Resources 2006. FAO, Rome.
Soil Fertility Manual 2006. International Plant Nutrition Institute.
Woch, F. (ed.) 2006. Wademekum klasyfikatora gleb. IUNG-PIB Puławy pp. 376.
Nitrogen Workshop 2012
Influence of agricultural practices and climate changes in Portuguese rice production Figueiredo, N.a, Carranca, C.a*, Trindade, H.b, Pereira, J.b, Prazeres, A.a, Mano, R.c, Marques, P.d, Vargues, A.a a INIA, Quinta do Marquês, Nova Oeiras, 2784-505 Oeiras, Portugal a,* Corresponding author: INIA, Qta. Marquês, Nova Oeiras, 2784-505 Oeiras; CEER, ISA/UTL, Portugal Tel. +351 214403517, Fax. +351 214416011, e.mail: firstname.lastname@example.org b CITAB, UTAD, Vila Real, Portugal c INIA, Tapada da Ajuda, Lisboa, Portugal d COTArroz, Paúl de Magos, Salvaterra de Magos, 2120-014 Salvaterra de Magos, Portugal
1. Background & Objectives Rice is one of the most important food crops in the world and the staple for more than half of the global population. Portugal is the first rice consumer, per capita, in Europe and the fourth producer (6 t ha-1), contributing to the 5.3% of the total European production. Rice cultivation in Portugal is intensive and is mostly located in the central and southern regions (Mondego, Tagus and Sado Valleys). The cultivation in Europe is mainly by flooding to control soil temperature, weeds and pests. The water content of soils can vary considerably, depending on climatic conditions, soil type and agricultural practices. In Portugal, rice straw is returned to the field after harvest, partially is burnt and partly is incorporated preceding the rice cultivation. Straw incorporation in soil in the non-rice-growing season can result in lower methane emission in the following rice-growing season than does the incorporation just before rice cultivation. The anaerobic conditions in flooded soils influence nitrogen (N) fertilizers dynamics, particularly the redox potential and soil pH. Rice roots absorb nitrate (NO3–) or ammonium (NH4+) from soil using a variety of transporters, but NH4+ is the preferential form in waterlogged soils. Nitrogen use efficiency is generally low (20-35%).
In 2011, we evaluated the soil and floodwater N and pH dynamics, and the rice response to the actual agricultural practices in an open field at Salvaterra de Magos (central Portugal), and in open top chambers with increased atmospheric carbon dioxide concentration [CO2] and temperature.
2. Materials & Methods In 2011, a field experiment was established in Salvaterra de Magos (central Portugal) with waterlogged rice (Oryza sativa L. ‘Ariete’) sown in May. A randomized block design with three replications was used to evaluate the soil and floodwater N and pH dynamics and crop response to the actual agricultural practices and to the double atmospheric [CO2] (560 ppm), and temperature increase. Six open top chambers (4 m wide x 3 m height x 2 m open top Ø) were installed, three for CO2 and three for temperature. The clay soil had a pH 4.7. The dominant clay minerals were illitessmectites. Irrigation water had a pH 7.9, a low electric conductivity (0.5 mS cm-1) and NH4+ content (0.2 mg NH4+-N l-1), a medium level of NO3– (5 mg NO3--N l-1) and a high amount of chloride (71 mg Cl- l-1) content. ‘Ariete’ is a moderate resistant cultivar to the Cl- toxicity. Mean air temperature during the growth cycle in the field varied from 12 ºC in March to 20 ºC in August.
Rainfall only occurred in June (25 mm) and October (230 mm). The wind speed was 3.8 - 8.1 m s-1.
Nitrogen fertilizers in the NH4+ and ureic forms were split twice as basal and top dressing (50 and 40 kg N ha-1, respectively). Inorganic-N and pH were frequently determined in soil and floodwater during the crop growth and for each treatment, and SPAD-measurements were taken in young Yrice leaf at each 2-3-week interval, in each plot. Results were analyzed using Main-Effects ANOVA (for floodwater composition) and General Linear Model for soil and plant.
Nitrogen Workshop 2012
3. Results & Discussion The floodwater above the soil surface did not show significant variations during the season as to inorganic-N content (1.61 mg NH4+-N l-1 and 0.99 mg NO3--N l-1) and pH (8.01). Soil pH increased with flooding but did not differ significantly with treatments. A slightly greater value was measured in CO2 chambers (pH 7.5) compared to pH 6.0 in other situations. The soil pH did not vary significantly along the vegetative growth and with depth, although a pH 7.5 was observed at surface, decreasing to pH 6.0 downwards. Soil inorganic-N varied significantly during the growth cycle. Ammonium was significantly higher in the open field (especially at the end of the season) and temperature chamber (average: 3.2 mg NH4+-N kg-1), but did not vary with soil depth (2.7 NH4+-N kg-1). The interaction date vs. depth affected significantly the cation content (Fig. 1a).
Nitrate decreased significantly in the soil profile, with a greater value at 0-20 cm (1.33 mg NO3--N kg-1), but did not vary with treatments (0.79 mg NO3--N kg-1). The interaction date vs. depth was significant (Fig. 1b), and the highest value was obtained in the top layer after the basal dressing.