Machine learning algorithms have been applied in the agriculture field to forecast crop productivity. Previous studies mainly focused on the whole crop growth period while different time windows on yield prediction were still unknown. The entire growth period was separated into each month to assess their corresponding predictive ability by taking maize production (silage and grain) in Czechia. We present a thorough assessment of county-level maize yield prediction in Czechia using a machine learning algorithm (extreme learning machine (ELM)) and an extensive set of weather data and maize yields from 2002 to 2018. Results show that sunshine in June and water deficit in July were vastly influential factors for silage maize yield. The two primary climate parameters for grain maize yield are minimum temperature in September and water deficit in May. The average absolute relative deviation (AARD), root mean square error (RMSE), and coefficient (R2) of the proposed models are 6.565–32.148%, 1.006–1.071%, 0.641–0.716, respectively. Based on the results, silage yield will decrease by 1.367 t/ha (3.826% loss), and grain yield will increase by 0.337 t/ha (5.394% increase) when the max temperature in May increases by 2 °C. In conclusion, ELM models show a great potential application for predicting maize yield.

In addition to industry, transport, and waste management, the agricultural sector is also a major emitter of CO₂ emissions. This article focuses on CO₂ equivalent emissions from soil in the context of mineral nitrogen fertiliser management. The methodology itself consists of several successive phases, the first of which is to determine basic statistical characteristics for all EU countries, primarily in terms of mineral nitrogen fertiliser consumption, but also in terms of the area of crops grown. EU countries with similar cropping patterns were selected for comparison so that the results could be compared. The results show that there are quite significant differences in CO₂ equivalent emissions between countries under similar conditions. At the same time, the values of the marginal increment of CO₂ emissions as a function of mineral nitrogen fertiliser consumption were calculated. On the basis of the results of the selected countries, an upper limit in terms of CO₂ emissions per hectare of arable land was also determined, and recommendations were made from a national perspective. The emissions themselves can be seen as a negative production externality that is not accounted for in the market mechanism and can thus also distort the price of agricultural production. The methodology used in this paper can be used to set an upper limit on CO₂ emissions from soil due to the use of mineral nitrogen fertilisers and can then be used as an indicator for regulating and defining future agricultural policy instruments within the EU, where the objective is to reduce the level of CO₂ emissions.

Major international institutions like the World Bank, African Union, and International Monetary Fund have made the development of robust governance systems a “sacred utterance” since the 1990s. Our study aimed to refute this common thinking by examining how governance structures have affected the expansion of the financial market in 47 African countries from 2008 to 2019. Using the availability of venture capital as a proxy for financial market development, our article departed from existing literature which used stock exchange characteristics as benchmarks for growth in the financial market. The governance variables comprise the six global governance indices: control of corruption, government effectiveness, political stability, regulatory quality, the rule of law, and voice and accountability. The model’s control variables include GDP per capita, inflation, and trade openness. Our finding suggests that a great quality governance climate is significant in explaining the growth of the financial market in Africa using the generalized method of moments (GMM) methodological approach with corrected standard errors. Considering these findings, our research makes the case that solid institutional frameworks might encourage the degree of financial systems growth in Africa. Therefore, the financial development rate in the African region will be significantly influenced by improving the quality of governance through strengthening legal and institutional frameworks to facilitate financial inclusion.

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The Czech Republic uses a dual system of agricultural land prices, namely the formal/official price—for tax purposes—and the market price in the case of a standard sale or purchase of farmland. This paper focuses on the construction of an econometric model to quantify the influence of soil hydrological characteristics on the production potential in a given climatic region. It also focuses on the use of this model for the determination of the official price of agricultural land, which is expressed on the basis of the code of the evaluated soil-ecological unit (ESEU) and based on defined soil characteristics. The pricing itself is based on the production potential of the land, which in practice is very important for spatial planning, as it determines the classes of agricultural land protection with regard to the possibility of setting aside agricultural land for non-productive purposes or for drawing subsidies for less favourable areas. In this context, the non-productive functions of agricultural land are also frequently discussed, especially its retention, which plays a very important role in the currently changing climatic conditions. There are a number of studies on soil retention, and numerous approaches to measuring it, but none of them address its impact on production potential and thus on the price of land. For this reason, this paper focuses on defining the influence of the retention of the main soil units (defined for the conditions of the Czech Republic) on production potential. For this definition, SUR models are used, where the endogenous variable is expressed as production potential and the exogenous variables include the basic soil characteristics such as grain size, porosity, hydrological component of the soil, and retention. The obtained outputs show both the high explanatory power of the model and the adequate parametric significance of most variables, which provides sufficient support for the use of the results in practice. In addition, the estimated models across all climatic regions are consistent with substantively logical assumptions about the link between production potential and soil hydrological properties, which secondarily demonstrates their applicability in practice, especially for state administration, but also for local government in the sense of municipalities, cities, and other organizational units.