Incorporating undesirable outputs in the operational assessments through the integration of Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) has received great attention recently. There are many studies throughout literature that apply various methods to integrate LCA and DEA. In this case study, the six most common approaches were employed to assess the winter wheat cropping system in Poland. These six methods were: a) ignoring undesirable outputs, b) treating undesirables as inputs to the DEA model, c) data transformation, d) impact rate, e) ratio model, and f) slack based measurement DEA with undesirable outputs. The environmental impact of wheat production was assessed by determining its carbon footprint (CF). The mean CF equalled 0.45 kg CO2eq per kg wheat grain (ranging from 0.25 to 0.67). According to the model comparison results, a slack based measurement DEA with undesirable outputs could better reflect the performance of undesirable outputs, and was selected as the most appropriate method to maximize the efficiency of winter wheat production while minimizing undesirable outputs. The advantage of applying the slack based model with undesirable outputs was that the targets presented by this model were based on existing efficient farms, as opposed to theoretical results; thus achieving these targets are feasible. The average efficiency score equalled 0.43, whereby few farms were classified as efficient farms. The results of the proposed integrated model showed a high reduction potential for mineral fertilizers (up to 595 kg ha-1 y-1), seed (up to 37 kg ha-1 y-1), and fuel (up to 75 L ha-1 y-1) in winter wheat farms. These results help farmers to obtain a realistic and reliable usage pattern for inputs in a winter wheat production system, whereby the greatest production can be achieved in conjunction with the lowest possible environmental impact.

Crop production relies on the use of natural resources and is a source of greenhouse gas (GHG) emissions. The present study uses survey data from 250 Polish farms to investigate the eco-efficiency of three main crops: winter wheat, winter triticale, and winter oilseed rape. First, the slack-based Data Envelopment Analysis (SBM-DEA) model with undesirable output (GHG emissions) was applied. In the next step, the Generalized Additive Model for Location, Scale and Shape (GAMLSS) was used to explain the efficiency scores. The calculated GHG emissions per hectare of crop were 1.9 tCO₂e, 3.2 tCO₂e, and 4.3 tCO₂e for winter triticale, wheat, and oilseed rape, respectively. Fully efficient farms used significantly less fertilizer (13.6–29.3%) and fuel (16.6–25.3%) while achieving higher yields (14.4–23.2%) and lower GHG emissions per hectare (10.8–17.7%). In practice, this means that efficient farms had a 20–32% lower carbon footprint per kilogram of yield than inefficient farms, depending on the crop. It was also shown that increasing the size of the cultivated area contributed to improving efficiency scores, while no conclusive evidence was found for an influence of economic size or farm type on their performance. Weather conditions had a significant impact on the efficiency score. In general, higher temperatures and precipitation in spring had a positive effect on efficiency, while an opposite relationship was observed in summer.

Echoing the bioenergy development initiative in Poland, high expectations are pinned on sweet sorghum usage for biogas plants. In contrast to its high profile in the industry, the research on the introduction and production of sorghum in Poland is lagging behind. To solve this issue, in this paper we have developed a spatial‐agent dynamic model of the agricultural land use and applied the model to eastern Poland. The model suggests that the economic and technical potential of sweet sorghum in this region is 6 and 7.5 million tonnes, respectively. Its introduction process largely follows the pattern of a typical industry life cycle, with the startup at the price of 8.20 €/tonne. Along with the market penetration of sorghum, a dramatic land use change of conventional crops can be foreseen, even with a land use competition among those crops. We believe that the exploitation of unutilized agricultural land resources and improving the yield of sorghum are helpful to alleviate this land use conflict. However, a higher food demand in the future and climate change may constrain the role of sorghum. This first comprehensive and high‐resolution study to its kind in Poland can help assess the country's bioenergy policies and contribute to the development of the biogas industry.

The aim of this study is to evaluate the efficiency of spring barley cultivation to indicate the reasons for its inefficiency and assess the possibility of the carbon footprint reduction potential. Survey data from 113 farms cultivating spring barley in 2016 were used. DEA (Data Envelopment Analysis) input oriented models were applied to assess technical, pure technical and scale efficiency. The carbon footprint of crop cultivation and its reduction potential for inefficient farms were estimated. The Fractional Regression Model (FRM) was used to explain how farm specific variables (structural and environmental factors) influence the efficiency of spring barley cultivation. Results indicate that the improvement of spring barley cultivation technology, through the effective use of inputs, especially mineral fertilizers, could lead to a reduction in the carbon footprint of its cultivation by an average of 32%, which, in turn, leads to a reduction in greenhouse gas emissions by 744 kg CO2e per ha. The economic size of farms, farm area, soil quality and annual rainfall significantly affect the results of technical efficiency.

Soil moisture under no-tillage and tillage systems in maize long-term experiment

Rolnictwo polskie zobowiązane jest do redukcji emisji amoniaku (NH3), w stosunku do 2005 r., o 1% corocznie w latach 2020-29 oraz o 17% corocznie od 2030 r. Znaczącym źródłem emisji jest stosowanie gnojowicy. W Polsce aplikuje się ją głównie rozbryzgowo na powierzchnię pola i przyoruje po 4-24 godzinach od aplikacji. W pracy zastosowano model ALFAM2 do scharakteryzowania emisji NH3 z rozbryzgowego stosowania gnojowicy w zależności od specyficznych dla Polski: dawek gnojowicy, jej suchej masy, temperatury powietrza, prędkości wiatru, opadów oraz pH gnojowicy. W stosunku do emisji NH3 z gnojowicy stosowanej na powierzchnię pola oszacowano emisje powstające przy aplikacji gnojowicy za pomocą wężów wleczonych, wężów z redlicami oraz aplikacji do otwartych szczelin, płytko doglebowo i głęboko doglebowo. Na podstawie uzyskanych wyników oszacowano wielkości redukcji emisji dla podstawowych praktyk jej aplikacji.