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We design an experiment to simulate how people make agricultural production decisions under three policy scenarios, each incorporating direct payments (DPs): (a) price uncertainty without countercyclical payments (CCPs); (b) price uncertainty with CCPs; and (c) price uncertainty, CCPs, and uncertainty regarding base acreage updating. Results are the CCP program and perceived possibility of future base updating created incentives for subjects to invest more in program (base) crops, despite payments being decoupled from current production decisions. Those choosing to reduce revenue risk by increasing plantings of base crops may face reduced incomes, suggesting the efficiency of crop markets may be diminished.

We analyze the coupling effect of expectations about base acreage and yield updating in future farm policy on production decisions in the presence of price, yield, and policy uncertainty for a risk-averse farmer producing a single crop. The farmer receives market revenue and government payments. Using stochastic dynamic programming, optimum decisions under present value calculations positively link current acreage and fertilizer decisions to future government payments through expected updates of base acreage and yield in the future regime. Moving from a zero probability of updating to a probability of one increases optimum acreage and yield by 6.25% and 0.134%, respectively.

The impact of recent changes to the U.S. commodity program and efficacy of environmental regulations designed to discourage continuous corn rotations will depend upon the value farmers place on corn base acreage. This paper estimates this value by assuming that the benefits of access to the program are capitalized into farmland rents. Using Iowa rental survey data and a hedonic pricing approach, the rent gradient for base acreage is found to be on the order of $12 per acre. The discounted stream of returns to base acreage suggests an asset value for corn base of approximately $200 per acre.

The 1985 Farm Bill departs from recent farm bills in moving toward more restrictive acreage control. The change from a two- to a five-year average in calculating base acreage and enforcement of limited cross-compliance appear to significantly alter crop mix decisions on representative Alabama cotton farms.

Sugarcane holds a critical position in global agriculture, serving as a basis for the sugar and bioenergy sectors. The integration of remote sensing technologies and sophisticated machine learning approaches and related models has revolutionized sugarcane research. These tools offer efficient, noninvasive, and large-scale assessment methods. This review highlights the utilization of satellite imagery and sensor data, encompassing RGB, multispectral, hyperspectral, and unmanned aerial vehicles (UAVs) in sugarcane agriculture. It addresses crop identification, pest and disease management, yield and acreage estimation, modeling, phenotypic measurement, and their impact on empowering farmers with insights for optimal irrigation, fertilizer application, and overall crop management. These advancements significantly increase productivity and foster environmental sustainability. The review had dual aims: (1) consolidate RS data applications in India’s sugarcane research and development, and (2) examine the pros and cons of RS and AI methods in sugarcane farming. The review employed prominent bibliographic databases—google scholar, scopus, researchgate, and web of science—along with pertinent research articles on RS and AI applications in sugarcane, and comprehensive data on sensors and UAVs retrieved from these databases. The study concludes that AI-driven crop RS stands as an effective method for monitoring and managing sugarcane, contributing significantly to improving yield and quality, while simultaneously offering substantial benefits in social, economic, and environmental realms. However, challenges in the sugar industry, such as adapting technology, high initial costs, climate impact, communication, policy, and regulation, must be addressed.

The impact of climate change continues to manifest itself daily in the form of extreme events and conditions such as droughts, floods, heatwaves, and storms. Better forecasting tools are mandatory to calibrate our response to these hazards and help adapt to the planet’s dynamic environment. Here, we present a deep convolutional residual regressive neural network (dcrrnn) platform called Flux to Flow (F2F) for discerning the response of watersheds to water-cycle fluxes and their extremes. We examine four United States drainage basins of varying acreage from smaller to very large (Bear, Colorado, Connecticut, and Mississippi). F2F combines model and ground observations of water-cycle fluxes in the form of surface runoff, subsurface baseflow, and gauged streamflow. We use these time series datasets to simulate, visualize, and analyze the watershed basin response to the varying climates and magnitudes of hydroclimatic fluxes in each river basin. Experiments modulating the time lag between remotely sensed and ground-truth measurements are performed to assess the metrological limits of forecasting with this platform. The resultant mean Nash–Sutcliffe and Kling–Gupta efficiency values are both greater than 90%. Our results show that a hydrological machine learning platform such as F2F can become a powerful resource to simulate and forecast hydroclimatic extremes and the resulting watershed responses and natural hazards in a changing global climate.

Dwarfing is one of the common phenotypic variations in asexually reproduced progeny of banana, and dwarfed banana is not only windproof and anti-fallout but also effective in increasing acreage yield. As a key gene in the strigolactone signalling pathway, DWARF53 (D53) plays an important role in the regulation of the height of plants. In order to gain insight into the function of the banana D53 gene, this study conducted genome-wide identification of banana D53 gene based on the M. acuminata, M. balbisiana and M. itinerans genome database. Analysis of MaD53 gene expression under high temperature, low temperature and osmotic stress based on transcriptome data and RT-qPCR was used to analyse MaD53 gene expression in different tissues as well as in different concentrations of GA and SL treatments. In this study, we identified three MaD53, three MbD53 and two MiD53 genes in banana. Phylogenetic tree analysis showed that D53 Musa are equally related to D53 Asparagales and Poales. Both high and low-temperature stresses substantially reduced the expression of the MaD53 gene, but osmotic stress treatments had less effect on the expression of the MaD53 gene. GR24 treatment did not significantly promote the height of the banana, but the expression of the MaD53 gene was significantly reduced in roots and leaves. GA treatment at 100 mg/L significantly promoted the expression of the MaD53 gene in roots, but the expression of this gene was significantly reduced in leaves. In this study, we concluded that MaD53 responds to GA and SL treatments, but “Yinniaijiao” dwarf banana may not be sensitive to GA and SL.

In Europe, there is an increasing interest in pulses both for their beneficial effects in cropping systems and for human health. However, despite these advantages, the acreage dedicated to pulses has been declining and their diversity has reduced, particularly in European temperate regions, due to several social and economic factors. This decline has stimulated a political debate in the EU on the development of plant proteins. By contrast, in Southern countries, a large panel of minor pulses is still cropped in regional patterns of production and consumption. The aim of this paper is to investigate the potential for cultivation of minor pulses in European temperate regions as a complement to common pulses. Our assumption is that some of these crops could adapt to different pedoclimatic conditions, given their physiological adaptation capacity, and that these pulses might be of interest for the development of innovative local food chains in an EU policy context targeting protein autonomy. The research is based on a systematic review of 269 papers retrieved in the Scopus database (1974–2019), which allowed us to identify 41 pulses as candidate species with protein content higher than 20% that are already consumed as food. For each species, the main agronomic (e.g., temperature or water requirements) and nutritional characteristics (e.g., proteins or antinutritional contents) were identified in their growing regions. Following their agronomic characteristics, the candidate crops were confronted with variability in the annual growing conditions for spring crops in Western European temperate areas to determine the earliest potential sowing and latest harvest dates. Subsequently, the potential sum of temperatures was calculated with the Agri4cast database to establish the potential climatic suitability. For the first time, 21 minor pulses were selected to be grown in these temperate areas and appear worthy of investigation in terms of yield potential, nutritional characteristics or best management practices.

The Eagle Creek watershed, a small subbasin (125 km2) within the Maumee River Basin, Ohio, was selected as a part of the Great Lakes Restoration Initiative (GLRI) “Priority Watersheds” program to evaluate the effectiveness of agricultural Best Management Practices (BMPs) funded through GLRI at the field and watershed scales. The location and quantity of BMPs were obtained from the U.S. Department of Agriculture-Natural Resources Conservation Service National Conservation Planning (NCP) database. A Soil and Water Assessment Tool (SWAT) model was built and calibrated for this predominantly agricultural Eagle Creek watershed, incorporating NCP BMPs and monitoring data at the watershed outlet, an edge-of-field (EOF), and tile monitoring sites. Input air temperature modifications were required to induce simulated tile flow to match monitoring data. Calibration heavily incorporated tile monitoring data to correctly proportion surface and subsurface flow, but calibration statistics were unsatisfactory at the EOF and tile monitoring sites. At the watershed outlet, satisfactory to very good calibration statistics were achieved over a 2-year calibration period, and satisfactory statistics were found in the 2-year validation period. SWAT fixes parameters controlling nutrients primarily at the watershed level; a refinement of these parameters at a smaller-scale could improve field-level calibration. Field-scale modeling results indicate that filter strips (FS) are the most effective single BMPs at reducing dissolved reactive phosphorus, and FS typically decreased sediment and nutrient yields when added to any other BMP or BMP combination. Cover crops were the most effective single, in-field practice by reducing nutrient loads over winter months. Watershed-scale results indicate BMPs can reduce sediment and nutrients, but reductions due to NCP BMPs in the Eagle Creek watershed for all water-quality constituents were less than 10%. Hypothetical scenarios simulated with increased BMP acreages indicate larger investments of the appropriate BMP or BMP combination can decrease watershed level loads.

Pesticide application in agricultural and residential areas is a worldwide practice. However, human pesticide poisoning and environmental pollution through pesticide residues remain a challenge in the developing world. The present study investigated the intensity of pesticide application in large-scale farms in Trans-Nzoia County to screen and prioritize the pesticides for potential human health and environmental risks. A cross-sectional survey involving 348 farmers was conducted in the study area, and data was analyzed using SPSS. Environmental Exposure Potential (EEP) and Toxicity Potentials (TP) were analyzed from the Pesticide Properties Database (PPDB). Majority (99.4%) of the farms surveyed apply various pesticide classes that include: organophosphates (34.78%), neonicotinoids (15.22%), carbamates (10.87%), pyrethroids (10.87%), organochlorines (8.7%), triazoles (6.5%), copper-based (4.34%), avermectines (2.17%), triazines (2.17%), and amidines (2.17%), with the use of organic manures (26.3%). Despite the high prevalence of pesticide application, only 48.28% of farms conduct soil quality monitoring, 77.3% of whom do not have clear records and schedules for conducting periodic soil analyses. There was a strong positive correlation between the acreage of operation and the use of herbicides in weed management (r = 0.77; p ≤ 0.05). In relation to degradation in the environment, 18.42% of the pesticides applied in the study area were persistent in soil sub-systems while 31.58% are persistent in water. Of the pesticides applied, 18.42% had high chances of bioconcentration in living tissues, 10.53% and 13.16% had the potential of contaminating groundwater and surface water resources, respectively. The ranked-order human toxicity potential associated with the used pesticides were teratogenicity (31.58%), neurotoxicity (28.95%), endocrine disruption (7.9%), carcinogenicity (7.9%), and mutagenicity (2.63%). However, 10.53% of the pesticides possess multiple toxicity potentials. Some farmers (53.70%) surveyed were not aware of the negative environmental impacts of pesticides with 59.50% having prior training on the use and handling of pesticides. Despite the availability of Personal Protective Equipment (PPEs) on larger farms, 31.9% of the farm workers do not adhere to their use during pesticide application. In conclusion, there is low awareness among farmers of human health and environmental risks associated with pesticide application. The study recommends training of farm managers, farm owners, and farm workers on pesticide handling and associated health and environmental effects.