Jouko Kleemola

Modelling crop growth and biomass partitioning to shoots and roots in relation to nitrogen and water availability, using a maximization principle

Many crop models relate the allocation of dry matter between shoots and roots exclusively to the crop development stage. Such models may not take into account the effects of changes in environment on allocation, unless the allocation parameters are altered. In this paper a crop model with a dynamic allocation parameter for dry matter between shoots and roots is described. The basis of the model is that a plant allocates dry matter such that its growth is maximized. Consequently, the demand and supply of carbon, nitrogen, and water is maintained in balance. This model supports the hypothesis that a functional equilibrium exists between shoots and roots.This paper explains the mathematical computation procedure of the crop model. Moreover, an analysis was made of the ability of a crop model to simulate plant dry matter production and allocation of dry matter between plant organs. The model was tested using data from a greenhouse experiment in which spring wheat (Triticum aestivum L.) was grown under different soil moisture and nitrogen (N) levels.Generally, the model simulations agreed well with data recorded for total plant dry matter. For validation data the coefficient of determination (r2) between simulated and measured shoot dry weight was 0.96. For the validation treatments r2 was slightly lower, 0.94. In addition to dry matter production the model succeeded satisfactorily in simulating the dry weight of different plant organs. The response of simulated root to shoot ratio to the level of soil moisture was mainly in accordance with the measured data. In contrast, the simulated ratio seemed to be insensitive to the changes in the levels soil N concentration used in the experiment.The data used in the present study were not extensive, and more data are needed to validate the model. However, the results showed that the model responses to the changes in soil N and water level were realistic and mostly agreed with the data. Thus, we suggest that the model and the method employed to allocate dry matter between roots and shoots are useful when modelling the growth of crops under N and water limited conditions.

Effect of Temperature on Phasic Development of Spring Wheat in Northern Conditions

Abstract The thermal response of spring wheat (Triticum aestivum L.) was examined using three cultivars grown in Finland. Field data was collected in 1977–88. Four different mathematical models were used to simulate the length of the development phases (sowing-to-heading, heading-to-yellow ripening and sowing-to-yellow ripening), and the accuracy of the models was compared. The thermal response of cvs. Heta and Kadett seems to be non-linear during the phase sowing-heading, while cv. Ruso reacts more linearly as temperature increases. For the other phases a linear model of response seems to be most accurate for all these cultivars. Mathematical simulation of the sowing-to-yellow ripening period was more accurate than for the other periods; its mean deviation with respect to the measured length of the phase (M D ) was 2.7 days per year on average. Simulation of the sowing-to-heading period (M D =3.3) was more accurate than for the heading-to-yellow ripening period (Mean D =5.3). The results suggest improved...

Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing

During 1996–2006, the Ministry of Agriculture and Forestry in Finland (MAFF), MTT Agrifood Research and the Finnish Geodetic Institute performed a joint remote sensing satellite research project. It evaluated the applicability of optical satellite (Landsat, SPOT) data for cereal yield estimations in the annual crop inventory program. Four Optical Vegetation Indices models (I: Infrared polynomial, II: NDVI, III: GEMI, IV: PARND/FAPAR) were validated to estimate cereal baseline yield levels (yb) using solely optical harmonized satellite data (Optical Minimum Dataset). The optimized Model II (NDVI) yb level was 4,240 kg/ha (R2 0.73, RMSE 297 kg/ha) for wheat and 4390 kg/ha (R2 0.61, RMSE 449 kg/ha) for barley and Model I yb was 3,480 kg/ha for oats (R2 0.76, RMSE 258 kg/ha). Optical VGI yield estimates were validated with CropWatN crop model yield estimates using SPOT and NOAA data (mean R2 0.71, RMSE 436 kg/ha) and with composite SAR/ASAR and NDVI models (mean R2 0.61, RMSE 402 kg/ha) using both reflectance and backscattering data. CropWatN and Composite SAR/ASAR & NDVI model mean yields were 4,754/4,170 kg/ha for wheat, 4,192/3,848 kg/ha for barley and 4,992/2,935 kg/ha for oats.

Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I)

During 1996–2006 the Ministry of Agriculture and Forestry in Finland, MTT Agrifood Research Finland and the Finnish Geodetic Institute carried out a joint remote sensing satellite research project. It evaluated the applicability of composite multispectral SAR and optical satellite data for cereal yield estimations in the annual crop inventory program. Three Vegetation Indices models (VGI, Infrared polynomial, NDVI and Composite multispetral SAR and NDVI) were validated to estimate cereal yield levels using solely optical and SAR satellite data (Composite Minimum Dataset). The average R2 for cereal yield (yb) was 0.627. The averaged composite SAR modeled grain yield level was 3,750 kg/ha (RMSE = 10.3%, 387 kg/ha) for high latitude spring cereals (4,018 kg/ha for spring wheat, 4,037 kg/ha for barley and 3,151 kg/ha for oats).

Improved sustainability of feedstock production with sludge and interacting mycorrhiza

Recycling nutrients saves energy and improves agricultural sustainability. Sewage sludge contains 2.6% P and 3.1% N, so the availability of these nutrients was investigated using four crops grown in either soil or sand. Further attention was paid to the role of mycorrhiza in improvement of nutrient availability. The content of heavy metals and metalloids in the feedstock was analyzed. Sewage sludge application resulted in greater biomass accumulation in ryegrass than comparable single applications of either synthetic fertilizer or digested sludge. Sewage sludge application resulted in more numerous mycorrhizal spores in soil and increased root colonization in comparison to synthetic fertilizer. All plants studied had mycorrhizal colonized roots, with the highest colonization rate in maize, followed by hemp. Sewage sludge application resulted in the highest P uptake in all soil-grown plants. In conclusion, sewage sludge application increased feedstock yield, provided beneficial use for organic wastes, and contributed to the sustainability of bioenergy feedstock production systems. It also improves the soil conditions and plant nutrition through colonization by mycorrhizal fungi as well as reducing leaching and need of synthetic fertilizers.

Forage and seed yield of winter turnip rape established as a mixed crop with cereals

(Received 6 June 2013; revised 22 November 2013; accepted 31 January 2014)SUMMARYCultivationofwinterturniprape(BrassicarapaL.ssp.oleifera(DC.)Metzg.)inFinlandhasbeenlimitedbecauseofits reputation as an unreliable crop and its mid-season sowing time of July, when fields are already sown to othercrops. An alternative management practice for winter turnip rape is proposed whereby it would be sown as amixed crop simultaneously with spring cereals. The growth and yield formation of winter turnip rape grown inmixed stands with four different spring cereals was studied in two field experiments conducted in 2009–11. Pureand mixed stands of winter turnip rape and spring cereals were established in May at two different cereal andwinter turnip rape stand densities. Subsequent to cereal harvest, one-third of each winter turnip rape plot washarvested for biomass in autumn, before cessation of growth. Three plant stand types, May- and July-sownmonocrops and a mixed crop with oat (Avena sativa L.) were sampled for forage analysis. Plant stand densitieswere monitored from establishment until maturity. Winter turnip rape yield and its quality, including oil content,protein content and thousand seed weight, were determined. Following favourable overwintering conditions,winter turnip rape established with cereals yielded comparably to that of pure stands in terms of both quantityand quality. However, a pure stand of winter turnip rape out-yielded mixed crop stands after unfavourableoverwintering conditions. Leaf removal decreased plant survival and seed yield. Establishing winter turnip rapewithacerealinMayisanalternativetosowingitasamonocropinJuly.Ahigherseedingrateisneededforunder-sown winter turnip rape. Furthermore, autumn-harvested winter turnip rape monocrop forage potentiallyrepresents a high-protein supplement for ruminants.INTRODUCTIONWinter turnip rape (Brassica rapa L. ssp. oleifera (DC.)Metzg.) is a cruciferous oilseed crop better adapted tothe Nordic climate than oilseed rape (Brassica napusL. ssp. oleifera (Moench) Metzg.). Because of its rapidgrowth, it out-competes weeds and consequentlysuitsorganic farming production systems (Makela et al.2011). Moreover, winter turnip rape can be harvestedbefore other crops because it reaches maturity earlierthanothercrops,i.e.inJuly,andsinceitisalsosowninJuly, it is in the ground for 12 months of the year. InFinland, winter turnip rape is sown by the end of Julybecause late sowing results in yield reduction (Valle1951). This is a limiting factor for winter turnip rapecultivationsincefarmsusuallyhavenofreelandatthattime because most field crops do not mature beforeAugust. A potential solution to this problem would beto sow winter turnip rape in May with a cereal andthereby reduce the costs of establishing the cropwhileincreasing land-use efficiency. The cereal would beharvested, as usual, in autumn and the winter turniprape would remain in the field to overwinter andproduce seed in the following growing season (Valle1951). Mixed crop stands often capture more mineralnutrients than pure stands (Midmore 1993; Morris &Garrity 1993a) and are also more water-use efficient(Morris & Garrity 1993b). This could also be the case

Modelling crop growth and biomass partitioning to shoots and roots in relation to nitrogen and water availability, using a maximization principle. II. Simulation of crop nitrogen balance

Abstract This study analysed of the ability of a crop model to simulate crop nitrogen (N) balance. The model was originally developed to serve as a foundation to develop a decision-making tool to analyse the impact of water management and nitrogen fertilization on crop yield. The model included a dynamic parameter for allocation of dry matter between root and shoot allowing root to shoot ratio to vary according to differing environmental conditions. The new allocation parameter was introduced in order to make the model more applicable under water and nitrogen limited growing conditions. Two wheat ( Triticum aestivum L.) data sets were used to test the model simulations. Generally, the model simulations agreed well with the recorded data on crop N uptake. The relationship between the actual and simulated amount of N taken up by the crop was close in the calibration treatments of a greenhouse experiment. The coefficient of determination ( r 2 ) of the regression line (simulated value=independent variable, measured value=dependent variable) was 0.90. The r 2 was 0.83 for the validation data. In the field experiments, the r 2 values were 0.91 for the calibration data and 0.82 for the validation data. In field data, the model underestimated in some cases the crop N uptake during the period when actual shoot dry weight increased exponentially in spring. Therefore, methods used in computation of nitrogen uptake have to be analysed further. Plant organ N content was simulated satisfactorily for both greenhouse and field data. However, the range over which the simulated values varied was larger than in the actual data. The results from the study indicate that our model is capable of simulating the crop N balance and we suggest that the model could be used when developing an N application decision tool for field crops. However, the availability of N and soil water were provided as inputs in the present study. Thus, the model should be integrated with models simulating below ground processes in the future. Moreover, the model should be further validated with actual field data.

Role of Potassium in Barley Plant Stand Architecture and Yield Formation

Potassium (K) plays a significant role in preventing outbreaks of pathogenic infections and thereby maintains the photosynthetic leaf area, which supplies assimilates for grain filling. The aim of this research was to investigate the role of K in barley straw anatomy, plant stand architecture, and yield formation. Growth and yield of barley improved with increasing K application rates. This appeared to be due to increased number of spike-bearing tillers, number of grains per spike, and single-grain weight. Straw and walls of cuticle, epidermis, and sclerenchymatous cells thickened in relation to applied K. Thick and firm internodes as well as efficient use of resources resulting from K applications are among key features for good-quality crop production in the field.