This paper investigates the impact of harvests and international markets on Swedish grain prices, 1665-1870. The paper finds that harvests at a national level had a greater impact on domestic grain prices than international grain prices. However, at a regional level, grain prices tended to be affected more by harvests outside the region. Furthermore, in the long term, foreign prices became a more important determinant of national grain prices. The conclusion is that, under certain circumstances, grain prices can be used as an indicator of harvest fluctuations and to construct historical national accounts, at least at a sufficiently aggregated level. Such an endeavour needs to be combined with a careful analysis of the impact of prices in the surrounding area.

This research explores the use of psychometric techniques to improve understanding of psychological mechanisms underlying judgment of excreta as fertilizer in agriculture including other excreta related activities. Participants consisted of environmental health students, smallholder farmers and traders in rural and urban Rwanda and Uganda. The finding reveals an inverse relationship between risk and benefit judgments. This relationship holds for the three groups of participants with significant risk-benefit correlations of p<.0001. This finding is consistent with other studies showing that affect plays a key role in risk perception, judgment and decision making.Building on this finding, we conclude that individuals with high risk and low benefit judgment for excreta related practices would eschew them or emphasize strict standards. Individuals with a high benefit and low risk judgment would engage in excreta management practices regardless of the actual risks involved. This finding is relevant for risk communication and risk management as it indicates that individuals do not rely only on risk management information they receive concerning excreta and related risks but also depend to an extent on their feelings about these substances when making judgments and decisions regarding the purpose for using excreta as fertilizer and the level of exposure they can tolerate and manage.

Potatis är en gröda med relativt ytligt rotsystem, vilket gör att den lätt utsätts för torkstress med sänkt skörd och kvalité som följd. Att bibehålla optimal markfukt är många gånger svårt och kan kräva att mätutrustningar och andra redskap används. Denna studie syftar till att klargöra effekten av en exakt markfuktsstyrning i potatis med hjälp av markfuktsensorer och prognosmodeller, samt sätta resultatet i relation till kostnaden för styrutrustningen. Studien bekräftar att både skörd, kvalitet och storleksfördelning påverkas av markfukten men också att sortskillnader finns. Skörden från de olika bevattningsleden skiljde sig signifikant från varandra i sorten Kuras, med högst skörd i de ”optimalt bevattnade” ledet (10-30 kPa) och lägst skörd i det obevattnade. Skörden av sorten Seresta följde i princip samma mönster som Kuras, men skillnaderna var inte signifikanta. De olika nivåerna i bevattningsintensitet påverkade inte i något fall stärkelsehalten signifikant, detta gäller oavsett år och sort. Ur ett ekonomiskt perspektiv så rekommenderas inte något styrsystem för bevattning inom stärkelsepotatisproduktion, detta eftersom priset på produkten är för lågt och skördeökningarna är för små för att täcka investeringskostnaderna. I matpotatis där priset och kraven på kvalitet är mycket högre lönar sig som regel alltid att optimera bevattningen.

Irrigation is often necessary to obtain a good economy on the farm. It can be hard to optimize irrigation even when the farmer has sufficient knowledge about the irrigation need and has the capacity to meet the need, due to uncertainty in weather forecasts and variability in selling prices for the crop. There are several technical tools to facilitate the decision-making regarding irrigation, such as forecasting models and soil moisture sensors. The evaluation assessed sevendifferent models and sensors. The Danish irrigation forecasting model and the Watermark sensor showed the best result and are recommended for decision making in irrigation of quality potato in order to increase profitability. It is not profitable to use the soil moisture sensors in starch potato for facilitating decision making in irrigation due to low price and low demands on quality of the tubers.

Potatis är en gröda med relativt ytligt rotsystem, vilket gör att den lätt utsätts för torkstress med sänkt skörd och kvalité som följd. Att bibehålla optimal markfukt är många gånger svårt och kan kräva att mätutrustningar och andra redskap används. Denna studie syftar till att klargöra effekten av en exakt markfuktsstyrning i potatis med hjälp av markfuktsensorer och prognosmodeller, samt sätta resultatet i relation till kostnaden för styrutrustningen. Studien bekräftar att både skörd, kvalitet och storleksfördelning påverkas av markfukten men också att sortskillnader finns. Skörden från de olika bevattningsleden skiljde sig signifikant från varandra i sorten Kuras, med högst skörd i de ”optimalt bevattnade” ledet (10-30 kPa) och lägst skörd i det obevattnade. Skörden av sorten Seresta följde i princip samma mönster som Kuras, men skillnaderna var inte signifikanta. De olika nivåerna i bevattningsintensitet påverkade inte i något fall stärkelsehalten signifikant, detta gäller oavsett år och sort. Ur ett ekonomiskt perspektiv så rekommenderas inte något styrsystem för bevattning inom stärkelsepotatisproduktion, detta eftersom priset på produkten är för lågt och skördeökningarna är för små för att täcka investeringskostnaderna. I matpotatis där priset och kraven på kvalitet är mycket högre lönar sig som regel alltid att optimera bevattningen

For developing countries, scarcity of climatic data is the biggest challenge, and model development with limited meteorological input is of critical importance. In this study, five data intelligent and hybrid metaheuristic machine learning algorithms, namely additive regression (AR), AR-bagging, AR-random subspace (AR-RSS), AR-M5P, and AR-REPTree, were applied to predict monthly mean daily reference evapotranspiration (ET0). For this purpose, climatic data of two meteorological stations located in the semi-arid region of Pakistan were used from the period 1987 to 2016. The climatic dataset includes maximum and minimum temperature (Tmax, Tmin), average relative humidity (RHavg), average wind speed (Ux), and sunshine hours (n). Sensitivity analysis through regression methods was applied to determine effective input climatic parameters for ET0 modeling. The results of performed regression analysis on all input parameters proved that Tmin, RHAvg, Ux, and n were identified as the most influential input parameters at the studied station. From the results, it was revealed that all the selected models predicted ET0 at both stations with greater precision. The AR-REPTree model was located furthest and the AR-M5P model was located nearest to the observed point based on the performing indices at both the selected meteorological stations. The study concluded that under the aforementioned methodological framework, the AR-M5P model can yield higher accuracy in predicting ET0 values, as compared to other selected algorithms.

Water productivity (WP) is one of the most important critical indicators in the essential planning of water consumption in the agricultural sector. For this purpose, the WP and economic water productivity (WPe) were estimated using agronomic technologies. The impact of agronomic technologies on WP and WPe was carried out in two parts of field monitoring and modeling using novel intelligent approaches. Extreme learning machine (ELM), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) methods were used to model WP and WPe. A dataset including 200 field data was collected from five treatment and control sections in the Malekan region, located in the southeast of Lake Urmia, Iran, for the crop year 2020–2021. Six different input combinations were introduced to estimate WP and WPe. The models used were evaluated using mean squared error (RMSE), relative mean squared error (RRMSE), and efficiency measures (NSE). Field monitoring results showed that in the treatment fields, with the application of agronomic technologies, the crop yield, WP, and WPe increased by 17.9%, 30.1%, and 19.9%, respectively. The results explained that irrigation water in farms W1, W2, W3, W4, and W5 decreased by 23.9%, 21.3%, 29.5%, 16.5%, and 2.7%, respectively. The modeling results indicated that the ANFIS model with values of RMSE = 0.016, RRMSE = 0.018, and NSE = 0.960 performed better in estimating WP and WPe than ANN and ELM models. The results confirmed that the crop variety, fertilizer, and irrigation plot dimensions are the most critical influencing parameters in improving WP and WPe.

Rapporten ger underlag för en pilot som planeras genomföras i Söderslätt Skåne under 2020-22.

Yield levels in smallholder farming systems in semi-arid sub-Saharan Africa are generally low. Water shortage in the root zone during critical crop development stages is a fundamental constraining factor. While there is ample evidence to show that conservation tillage can promote soil health, it has recently been suggested that the main benefit in semi-arid farming systems may in fact be an in situ water harvesting effect. In this paper we present the result from an on-farm conservation tillage experiment (combining ripping with mulch and manure application) that was carried out in northeastern Tanzania from 2005 to 2008, testing this hypothesis. Special attention was given to the effects on the water retention properties of the soil. The tested conservation treatment only had a clear yield increasing effect during one of the six experimental seasons (maize grain yields increased by 41%, and biomass by 65%), and this was a season that received exceptional amounts of rainfall (549 mm). While the other seasons provided mixed results, there seemed to be an increasing yield gap between the conservation tillage treatment and the control towards the end of the experiment. Regarding soil system changes, small but significant effects on chemical and microbiological properties, but not on physical properties, were observed. This raises questions about the suggested water harvesting effect and its potential to contribute to stabilized yield levels under semi-arid conditions. We conclude that, at least in a shorter time perspective, the tested type of conservation tillage seems to boost productivity during already good seasons, rather than stabilize harvests during poor rainfall seasons. Highlighting the challenges involved in upgrading these farming systems, we discuss the potential contribution of conservation tillage towards improved water availability in the crop root zone in a longer-term perspective.

This is the input dataset to a Python script (https://github.com/oskeng/MF-bio-grass) used to model the effects of widespread deployment of grass in rotations with annual crops to provide biomass while remediating soil organic carbon (SOC) losses and other environmental impacts.

For more information about the dataset and the study, see the original article:

Englund, O., Mola-Yudego, B., Börjesson, P., Cederberg, C., Dimitriou, I., Scarlat, N., Berndes, G. Large-scale deployment of grass in crop rotations as a multifunctional climate mitigation strategy. GCB Bioenergy

Usage Notes:

The data file (Geopackage) can be opened using standard GIS software, preferably GRASS GIS or QGIS (both open source).This dataset is intended as input to a Python script (https://github.com/oskeng/MF-bio-grass) that must be run from within a GRASS GIS session.

Society faces the double challenge of increasing biomass production to meet the future demands for food, materials and bioenergy, while addressing negative impacts of current (and future) land use. In the discourse, land use change (LUC) has often been considered as negative, referring to impacts of deforestation and expansion of biomass plantations. However, strategic establishment of suitable perennial production systems in agricultural landscapes can mitigate environmental impacts of current crop production, while providing biomass for the bioeconomy. Here, we explore the potential for such “beneficial LUC” in EU28. First, we map and quantify the degree of accumulated soil organic carbon losses, soil loss by wind and water erosion, nitrogen emissions to water, and recurring floods, in ∼81.000 individual landscapes in EU28. We then estimate the effectiveness in mitigating these impacts through establishment of perennial plants, in each landscape. The results indicate that there is a substantial potential for effective impact mitigation. Depending on criteria selection, 10–46% of the land used for annual crop production in EU28 is located in landscapes that could be considered priority areas for beneficial LUC. These areas are scattered all over Europe, but there are notable “hot-spots” where priority areas are concentrated, e.g., large parts of Denmark, western UK, The Po valley in Italy, and the Danube basin. While some policy developments support beneficial LUC, implementation could benefit from attempts to realize synergies between different Sustainable Development Goals, e.g., “Zero hunger”, “Clean water and sanitation”, “Affordable and Clean Energy”, “Climate Action”, and “Life on Land”.

The agricultural sector can contribute to climate change mitigation by reducing its own greenhouse gas (GHG) emissions and sequestering atmospheric carbon in vegetation and soils, and by providing biomass for substituting fossil fuels and other GHG intensive products in the energy, industry and transport sectors. New policies at EU level provide incentives for more sustainable land use practices, for example, cultivation systems using perennial plants that provide biomass for food, bioenergy and other biobased products along with land carbon sequestration and other environmental benefits. Based on spatial modelling across more than 81,000 landscapes in Europe, we find that introduction of grass-clover leys into rotations with annual crops could result in soil organic carbon sequestration corresponding to 5-10% of total current GHG emissions from agriculture in EU27+UK, annually until 2050. The combined annual GHG savings from soil carbon sequestration and use of biogas produced in connection to grass-based biorefineries equals 13-48% of current GHG emissions from agriculture. The assessed environmental co-benefits (reduced wind and water erosion, reduced nitrogen emissions to water, and mitigation of impacts associated with flooding) are considerable. Besides policy instruments, new markets for grass biomass, e.g., as feedstock for producing biofuels and protein concentrate, can incentivize widespread deployment of in-rotation grass cultivation.

The agriculture sector can contribute to climate change mitigation by reducing its own greenhouse gas (GHG) emissions, sequestering carbon in vegetation and soils, and providing biomass to substitute for fossil fuels and other GHG intensive products. The sector also needs to address water, soil, and biodiversity impacts caused by historic and current practices. Emerging EU policies create incentives for cultivation of perennial plants that provide biomass along with environmental benefits. One such option, common in northern Europe, is to include grass in rotations with annual crops to provide biomass while remediating soil organic carbon (SOC) losses and other environmental impacts. Here, we apply a spatially explicit model on >81,000 sub-watersheds in EU27+UK (Europe) to explore the effects of widespread deployment of such systems. Based on current accumulated SOC losses in individual sub-watersheds, the model identifies and quantifies suitable areas for increased grass cultivation and corresponding biomass- and protein supply, SOC sequestration, and reductions in nitrogen emissions to water as well as wind and water erosion. The model also provides information about possible flood mitigation. The results indicate a substantial climate mitigation potential, with combined annual GHG savings from soil-carbon sequestration and displacement of natural gas with biogas from grass-based biorefineries, equivalent to 13-48% of current GHG emissions from agriculture in Europe. The environmental co-benefits are also notable, in some cases exceeding the estimated mitigation needs. Yield increases for annual crops in modified rotations mitigate the displacement effect of increasing grass cultivation. If the grass is used as feedstock in lieu of annual crops, the displacement effect can be negative, i.e., a reduced need for annual crop production elsewhere. Incentivizing widespread deployment will require supportive policy measures as well as new uses of grass biomass, e.g., as feedstock for green biorefineries producing protein concentrate, biofuels and other biobased products.

In the project "Concept study battery-powered autonomous agricultural machine", an electric autonomous agricultural machine has been compared with a conventional diesel tractor by simulating all machine activities in a field on an organic dairy farm of 200 hectares during one year. Combine Harvesting was not included. The results show that it is possible to replace a conventional tractor (160 kW) with two autonomous battery-powered machines (36 kW motor, 113 kWh battery) with 15% lower costs. Even better, energy consumption was reduced by 58% and greenhouse gas emissions by 92% compared to diesel when energy consumption and greenhouse gas emissions from battery manufacturing were included. Furthermore local emissions were avoided and the sound pollution greatly reduced.  The major efficiency increase can be explained by the higher efficiency of electric power drivelines, and that the Swedish and Nordic electricity mix has very low greenhouse gas emissions and environmental impact. We also see that the weight of the conventional diesel tractor is more than four times the weight of the autonomous battery-powered, and that's only the tractor without any implements or payload. In the sensitivity calculations, changes in the cost of implements or driver / operator costs have the greatest impact on the total price. It is also important not to have a charger with low power, while the battery price does not have such big impact on the total cost. Continued research is needed to verify the theoretical simulation by building a test platform where knowledge can be gathered about the problems and opportunities in practical work - both in the field of battery-electric operation and autonomous driving.

The aim of this thesis is to identify how forest management in Tanzania can contribute to global climate change mitigation while improving livelihoods of forest-dependent communities.

A mechanism for reducing emissions from deforestation and forest degradation (REDD) is meant to slow increases of atmospheric CO₂ while channelling funds to developing countries. In Tanzania, pioneering work in participatory forest management (PFM) has promoted local-level control over forest resource use. The purpose of this study is to contribute to a linkage between REDD and PFM that maximises benefits for communities, forests and global climate.

Three PFM projects with relation to REDD have been studied, primarily using semi-structured interviews with villagers, district officials, project facilitators, researchers, consultants and policy-makers. Analysis consists of comparing experiences at different levels and putting them in the theoretical context of climate change and forest conservation.

The study identifies several issues: local and central government institutions cannot ensure equitable benefit sharing; cross-sectoral co-ordination to address fundamental causes of the problems is lacking; participation of local communities is not satisfactory.

However, the process is at an early stage. Current activities will hopefully contribute to a future framework that properly addresses these and other obstacles. If this is accomplished, PFM and REDD can complement each other in a positive way.

Temperature profoundly affects saprotrophic respiration rates, and carbon quality theory predicts that the rates' temperature Sensitivity should increase as the quality of the carbon source declines. However, reported relationships between saprotrophic respiration responses to temperature and carbon quality vary widely. Some of this variability may arise from confounding effects related to both substrate quality and substrate availability. The importance of these variables, as well as substrate diffusion and uptake rates, for the temperature sensitivity of saprotrophic respiration has been validated theoretically, but not empirically demonstrated. Thus, we tested effects of varying substrate uptake rates on the temperature sensitivity of organic carbon degradation. For this purpose we created a model system using the organic layer (O-horizon), of a boreal forest soil, specifically to test effects of varying monomer uptake and release rates. The addition of both monomers and polymers generally increased the temperature sensitivity of saprotrophic respiration. In response to added monomers, there was a linear increase in the temperature sensitivity of both substrate-induced respiration and the specific growth rate with increasing rate of substrate uptake as indicated by the CO2 production at 14 degrees C. Both of these responses diverge from those predicted by the carbon quality theory, but they provide the first empirical evidence consistent with model predictions demonstrating increased temperature sensitivity with increased uptake rate of carbon monomers over the cell membrane. These results may explain why organic material of higher carbon quality induces higher temperature responses than lower carbon quality compounds, without contradicting carbon quality theory. 

Agricultural damage by geese is a growing problem in Europe and farmers play a key role in the emerging multilevel adaptive management system. This study explored how characteristics associated with the farmer and the farm, along with experience of damage, cognitive appraisals, emotions, and management beliefs were associated with the perceived adaptive capacity of the goose management system among farmers in the south of Sweden (n = 1,067). Survey results revealed that owning a larger farm, a farm closer to water or formally protected areas, along with cultivating cereal and root crops, were associated with geese evoking stronger negative emotions. Further, more previous experience of damage was related to stronger negative emotions and lower levels of perceived adaptive capacity. However, even more important determinants of perceived adaptive capacity were cognitive appraisals, emotions, and management beliefs. Bridging the ties between individual farmers and the system is important for improved multilevel management.