Hyperspectral field measurements to determine dry matter yield and nutritive value of legume-grass swards

Summary: Productivity, botanical composition and forage quality of legume-grass swards are important factors for successful arable farming in both organic and conventional farming systems. As these attributes can vary considerably within a field, a non-destructive method of detection while doing other tasks would facilitate a more targeted management of crops, forage and nutrients in the soil-plant-animal system. This study was undertaken to explore the potential of field spectral measurements for a non destructive prediction of dry matter (DM) yield, legume proportion in the sward, metabolizable energy (ME), ash content, crude protein (CP) and acid detergent fiber (ADF) of legume-grass mixtures. Two experiments were conducted in a greenhouse under controlled conditions which allowed collecting spectral measurements which were free from interferences such as wind, passing clouds and changing angles of solar irradiation. In a second step this initial investigation was evaluated in the field by a two year experiment with the same legume-grass swards. Several techniques for analysis of the hyperspectral data set were examined in this study: four vegetation indices (VIs): simple ratio (SR), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and red edge position (REP), two-waveband reflectance ratios, modified partial least squares (MPLS) regression and stepwise multiple linear regression (SMLR). The results showed the potential of field spectroscopy and proved its usefulness for the prediction of DM yield, ash content and CP across a wide range of legume proportion and growth stage. In all investigations prediction accuracy of DM yield, ash content and CP could be improved by legume-specific calibrations which included mixtures and pure swards of perennial ryegrass and of the respective legume species. The comparison between the greenhouse and the field experiments showed that the interaction between spectral reflectance and weather conditions as well as incidence angle of light interfered with an accurate determination of DM yield. Further research is hence needed to improve the validity of spectral measurements in the field. Furthermore, the developed models should be tested on varying sites and vegetation periods to enhance the robustness and portability of the models to other environmental conditions.

Flächenhafte und funktionale Analyse kleinräumiger standortbedingter Variationen der Grünlandvegetation und -produktivität

Zur Abbildung heterogener Standorteigenschaften und Ertragspotenziale werden zunehmend flächenhafte Daten nachgefragt. Insbesondere für Grünland, das häufig durch ausgeprägte Standortheterogenität gekennzeichnet ist, ergeben sich hohe Anforderungen an die Wiedergabequalität, denn die realen Verhältnisse sollen in praktikabler Weise möglichst exakt abgebildet werden. Außerdem können flächenhafte Daten genutzt werden, um Zusammenhänge zwischen teilflächenspezifischen Standorteigenschaften und Grünlandaspekten detaillierter zu analysieren und bisher nicht erkannte Wechselbeziehungen nachzuweisen. Für mitteleuropäisches Grünland lagen zu Beginn dieser Arbeit derartige räumliche Untersuchungen nicht oder nur in Teilaspekten vor. Diese Arbeit befasste sich mit der Analyse von Wirkungsbeziehungen zwischen Standort- und Grünlandmerkmalen auf einer im Nordhessischen Hügelland (Deutschland) weitgehend praxisüblicher bewirtschafteten 20 ha großen Weidefläche. Erhoben wurden als Standortfaktoren die Geländemorphologie, die Bodentextur, die Grundnährstoffgehalten sowie als Parameter des Grünlandbestandes die botanische Zusammensetzung, der Ertrag und die Qualitätsparameter. Sie wurden sowohl in einem 50 m-Raster ganzflächig, als auch auf drei 50x50 m großen Teilflächen in erhöhter Beprobungsdichte (6,25 m-Rasterweite) aufgenommen. Die relevanten Fragestellungen zielen auf die räumliche und zeitliche Variabilität von Grünlandbestandesparametern innerhalb von Grünlandflächen sowie deren Abhängigkeit von den Standortfaktoren. Ein weiterer Schwerpunkt war die Überprüfung der Frage, ob die reale Variabilität der Zielvariablen durch die Interpolierung der punktuell erfassten Daten wiedergegeben werden kann. Die Beziehungen zwischen Standort- und Grünlandmerkmalen wurden mit monokausalen und multivariaten Ansätzen untersucht. Die Ergebnisse ließen, unabhängig vom Jahreseinfluss, bereits bestimmte Zusammenhänge zwischen botanischer Zusammensetzung und Standort, auch auf dem untersuchten kleinen Maßstab innerhalb der Grünlandfläche, finden. Demzufolge können unterschiedliche Areale abgegrenzt und charakterisiert werden, die als Grundlage für Empfehlungen zur Ausweisung von Arealen zur teilspezifischen Bewirtschaftung erarbeitet wurden. Die Validierung der interpolierten Daten zeigte, dass die 50-m Rasterbeprobung nur eine begrenzte Wiedergabe der räumlichen Variabilität ermöglicht. Inwieweit derartige Beziehungen quantitativ genauer beschreibbar sind, bleibt auf Grund der verbliebenen unerklärten Varianz im Datensatz dieser Studie offen.

Determination of yield and quality parameters of energy crops applying laboratory and field spectroscopy

A real-time analysis of renewable energy sources, such as arable crops, is of great importance with regard to an optimised process management, since aspects of ecology and biodiversity are considered in crop production in order to provide a sustainable energy supply by biomass. This study was undertaken to explore the potential of spectroscopic measurement procedures for the prediction of potassium (K), chloride (Cl), and phosphate (P), of dry matter (DM) yield, metabolisable energy (ME), ash and crude fibre contents (ash, CF), crude lipid (EE), nitrate free extracts (NfE) as well as of crude protein (CP) and nitrogen (N), respectively in pretreated samples and undisturbed crops. Three experiments were conducted, one in a laboratory using near infrared reflectance spectroscopy (NIRS) and two field spectroscopic experiments. Laboratory NIRS measurements were conducted to evaluate to what extent a prediction of quality parameters is possible examining press cakes characterised by a wide heterogeneity of their parent material. 210 samples were analysed subsequent to a mechanical dehydration using a screw press. Press cakes serve as solid fuel for thermal conversion. Field spectroscopic measurements were carried out with regard to further technical development using different field grown crops. A one year lasting experiment over a binary mixture of grass and red clover examined the impact of different degrees of sky cover on prediction accuracies of distinct plant parameters. Furthermore, an artificial light source was used in order to evaluate to what extent such a light source is able to minimise cloud effects on prediction accuracies. A three years lasting experiment with maize was conducted in order to evaluate the potential of off-nadir measurements inside a canopy to predict different quality parameters in total biomass and DM yield using one sensor for a potential on-the-go application. This approach implements a measurement of the plants in 50 cm segments, since a sensor adjusted sideways is not able to record the entire plant height. Calibration results obtained by nadir top-of-canopy reflectance measurements were compared to calibration results obtained by off-nadir measurements. Results of all experiments approve the applicability of spectroscopic measurements for the prediction of distinct biophysical and biochemical parameters in the laboratory and under field conditions, respectively. The estimation of parameters could be conducted to a great extent with high accuracy. An enhanced basis of calibration for the laboratory study and the first field experiment (grass/clover-mixture) yields in improved robustness of calibration models and allows for an extended application of spectroscopic measurement techniques, even under varying conditions. Furthermore, off-nadir measurements inside a canopy yield in higher prediction accuracies, particularly for crops characterised by distinct height increment as observed for maize.

Yield gap analysis and assessment of climate-induced yield trends of irrigated rice in selected provinces of the Philippines

This study describes a combined empirical/modeling approach to assess the possible impact of climate variability on rice production in the Philippines. We collated climate data of the last two decades (1985-2002) as well as yield statistics of six provinces of the Philippines, selected along a North-South gradient. Data from the climate information system of NASA were used as input parameters of the model ORYZA2000 to determine potential yields and, in the next steps, the yield gaps defined as the difference between potential and actual yields. Both simulated and actual yields of irrigated rice varied strongly between years. However, no climate-driven trends were apparent and the variability in actual yields showed no correlation with climatic parameters. The observed variation in simulated yields was attributable to seasonal variations in climate (dry/wet season) and to climatic differences between provinces and agro-ecological zones. The actual yield variation between provinces was not related to differences in the climatic yield potential but rather to soil and management factors. The resulting yield gap was largest in remote and infrastructurally disfavored provinces (low external input use) with a high production potential (high solar radiation and day-night temperature differences). In turn, the yield gap was lowest in central provinces with good market access but with a relatively low climatic yield potential. We conclude that neither long-term trends nor the variability of the climate can explain current rice yield trends and that agroecological, seasonal, and management effects are over-riding any possible climatic variations. On the other hand the lack of a climate-driven trend in the present situation may be superseded by ongoing climate change in the future.

A pilot investigation of the relationship between climate variability and milk compounds under the bootstrap technique

This study analyzes the linear relationship between climate variables and milk components in Iran by applying bootstrapping to include and assess the uncertainty. The climate parameters, Temperature Humidity Index (THI) and Equivalent Temperature Index (ETI) are computed from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis (2002–2010). Milk data for fat, protein (measured on fresh matter bases), and milk yield are taken from 936,227 milk records for the same period, using cows fed by natural pasture from April to September. Confidence intervals for the regression model are calculated using the bootstrap technique. This method is applied to the original times series, generating statistically equivalent surrogate samples. As a result, despite the short time data and the related uncertainties, an interesting behavior of the relationships between milk compound and the climate parameters is visible. During spring only, a weak dependency of milk yield and climate variations is obvious, while fat and protein concentrations show reasonable correlations. In summer, milk yield shows a similar level of relationship with ETI, but not with temperature and THI. We suggest this methodology for studies in the field of the impacts of climate change and agriculture, also environment and food with short-term data.

Towards an assessment of on-farm niches for improved forages in Sud-Kivu, DR Congo

Inadequate quantity and quality of livestock feed is a persistent constraint to productivity for mixed crop-livestock farming in eastern Democratic Republic of Congo. To assess on-farm niches of improved forages, demonstration trials and participatory on-farm research were conducted in four different sites. Forage legumes included Canavalia brasiliensis (CIAT 17009), Stylosanthes guianensis (CIAT 11995) and Desmodium uncinatum (cv. Silverleaf), while grasses were Guatemala grass (Tripsacum andersonii), Napier grass (Pennisetum purpureum) French Cameroon, and a local Napier line. Within the first six months, forage legumes adapted differently to the four sites with little differences among varieties, while forage grasses displayed higher variability in biomass production among varieties than among sites. Farmers’ ranking largely corresponded to herbage yield from the first cut, preferring Canavalia, Silverleaf desmodium and Napier French Cameroon. Choice of forages and integration into farming systems depended on land availability, soil erosion prevalence and livestock husbandry system. In erosion prone sites, 55–60% of farmers planted grasses on field edges and 16–30% as hedgerows for erosion control. 43% of farmers grew forages as intercrop with food crops such as maize and cassava, pointing to land scarcity. Only in the site with lower land pressure, 71% of farmers grew legumes as pure stand. When land tenure was not secured and livestock freely roaming, 75% of farmers preferred to grow annual forage legumes instead of perennial grasses. Future research should develop robust decision support for spatial and temporal integration of forage technologies into diverse smallholder cropping systems and agro-ecologies.

The Effect of Climate Variability on Main Components of Cow Milk in Iran

The main purpose of this study is to assess the relationship between six bioclimatic indices for cattle (temperature humidity (THI), environmental stress (ESI), equivalent temperature (ESI), heat load (HLI), modified heat load (HLInew) and respiratory rate predictor(RRP)) and fundamental milk components (fat, protein, and milk yield) considering uncertainty. The climate parameters used to calculate the climate indices were taken from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis from 2002 to 2010. Cow milk data were considered for the same period from April to September when cows use natural pasture, with possibility for cows to choose to stay in the barn or to graze on the pasture in the pasturing system. The study is based on a linear regression analysis using correlations as a summarizing diagnostic. Bootstrapping is used to represent uncertainty estimation through resampling in the confidence intervals. To find the relationships between climate indices (THI, ETI, HLI, HLInew, ESI and RRP) and main components of cow milk (fat, protein and yield), multiple liner regression is applied. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Cross validation is used to avoid over-fitting. Based on results of investigation the effect of heat stress indices on milk compounds separately, we suggest the use of ESI and RRP in the summer and ESI in the spring. THI and HLInew are suggested for fat content and HLInew also is suggested for protein content in the spring season. The best linear models are found in spring between milk yield as predictands and THI, ESI,HLI, ETI and RRP as predictors with p-value < 0.001 and R2 0.50, 0.49. In summer, milk yield with independent variables of THI, ETI and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. It is strongly suggested that new and significant indices are needed to control critical heat stress conditions that consider more predictors of the effect of climate variability on animal products, such as sunshine duration, quality of pasture, the number of days of stress (NDS), the color of skin with attention to large black spots, and categorical predictors such as breed, welfare facility, and management system. This methodology is suggested for studies investigating the impacts of climate variability/change on food quality/security, animal science and agriculture using short term data considering uncertainty or data collection is expensive, difficult, or data with gaps.